Since technology has started disrupting the healthcare sector, innovations are included to follow a patient-driven approach. Recently, AI chatbots have been the new hue in the market and have caught the attention of health experts. As a vital part of healthcare IT solutions, AI chatbots work faster and more effectively to resolve patients’ queries than traditional calling systems. In this article, we have presented all the benefits of including AI chatbots in the healthcare system. So, without further ado, let’s begin!
Challenges in Healthcare
The Healthcare industry needs official reports of patients, and thus, user privacy is always at stake. Moreover, since they know they are talking to an intelligent and smart chatbot, patients find it challenging to build their trust and share their personal information with an online chatbot.
Thus, different data safety methods must be implemented to stay ahead of cybercrimes that steal user’s private data and follow best practices for reliable AI implementation. If you have a healthcare platform, business owners always look at implementing suitable data safety measures to strengthen their platform’s resistance to cybersecurity.
AI Chatbots As Part Of Healthcare IT Solutions
AI Chatbots are mainly the software that is developed using machine learning algorithms, such as NLP. Therefore, they are super helpful in engaging a conversation with any user for fulfilling the sole aim of providing excellent real-time patient assistance
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All the medical assistants in the healthcare industry are switching to AI-enabled tools that impart superb assistance at low costs. Suppose you tend to use any healthcare app or visit some medical website and find a conversation with any medical expert who sounds human. In that case, it is an intelligent AI chatbot catering to your specific needs.
All the patients like to speak to real qualified doctors or medical specialists, and AI chatbots can achieve this. The best part is that many chatbots that comprise complex self-learning algorithms are known for maintaining a comprehensive human-like conversation and assist perfectly.
Some Prominent Use Cases Of AI Chatbots In Healthcare
Given below are all the top use cases of AI chatbots in the future healthcare industry.
1. Boost Patient Engagement
Since chatbots are intelligent, they are helpful in providing accurate suggestions according to the specific interest of the patient. Not just this, they both keep a regular touch with both health officials as well as patients. Thus, they serve as the bridge between patients and health professionals to offer simplified consulting.
2. Top-Notch Customer Service
AI Chatbots were launched to redefine the customer service arena in all industries. For example, in healthcare, these chatbots are pretty helpful in scheduling appointments, sharing feedback, issuing reminders, and noting the information regarding refilling prescription medications.
3. Offer updates to patients
Patients who are looking forward to getting surgery can easily connect with the chatbot to help in the preparation of the surgery. Once the appointment is booked, the chatbot confirms the appointment through a confirmation mail or text message.
Even during and after the surgery, different details regarding the surgery are shared with the patient’s family. It will also help in sharing educational materials about the surgery.
4. Voice assistance
AI-enabled chatbots are not just text-based assistants; they also assist through their voice. All the health-related guidance and results are communicated via chatbots. Many times, textual conversations may become a little difficult to discuss a patient’s problems. Thus, to understand and render the best support with customized solutions, AI chatbots are extensively used to solve patient’s issues of any size. If you get to experience this service, you will fall for such an innovative and advanced way of connecting both the patient and the medical representative.
5. Informative Chatbots
Some chatbots are also present to proffer excellent information on different medical issues. For example, automated details on other conditions get popped up quickly on all health-centric apps. A plethora of medical websites use these types of chatbots to render quick information on different topics efficiently. For example, if you want to use customer support to know about breast cancer, the chatbots will provide information based on top links on the search engine.
Conclusion
AI chatbots hold a bright future in the healthcare domain. Along with other healthcare IT solutions, AI chatbots will further revolutionize and create a strong hole in this realm. Don’t worry; these chatbots do not delve deeper into providing medical aid; however, they are emerging as a reliable medium to boost patient’s engagement with their respective health providers.
Nothing has been quite so transformative during the last two years as the way we work. The future of work and our mobility as knowledge workers has been augmented by digital transformation. Ready to have the best of both worlds? Well now you can.
This article will explore many of those familiar clichés of the benefits of remote work for the knowledge worker. Many forget it’s not just for the individual programmer, software engineer or back-end knowledge worker, but also is better for organizations, firms, and their industrial psychology.
The pandemic brought us many challenges including lockdowns, physical separation, uncertainty, loneliness, a shift in how we spend our time and relate, even with family and, for some of the lucky few of us, remote work. So what are the benefits of remote work? Do the benefits outweigh the potential mental health and salary costs? While this is an immensely personal weighing, society continues to churn from the office in the ‘great resignation’.
Remote work certainly seems tethered with the future of work for various knowledge workers including those in programming, datascience, machine learning and so forth.
Heading into the second half of 2021 and into 2022, how will remote work feel as the new normal and what benefits will we no longer be able to live without. Do you know someone in the office of in management who is skeptical of the benefits for the firm or for workers? Share with them this article.
More Life-Work Balance: More Quality Shared Time
Ihave been enjoying work from home. It has allowed me more time with the family. No commute in themorning, no commute in the evening. Still put the hours in, but my breaks consist of family time. When I was in the office, I was lucky to get an hour with the family on a weekday. Enjoy the new house. Have fun exploring CO, so much to do there. We miss it. Hoping we stay remote, but as of now, that is planned to mostly end in September for me.–Richard Litsky.
More time with family is the single most reported benefit of more hybrid work or remote work arrangements. This can lead to healthier family units, marked improvements in spousal relationships and more social support when facing the ups and downs of life.
Digital Nomad Lifestyle: More Housing Opportunities
The pandemic really has shown that remote working is a viable option insomany ways including having the ability to buy a place of your own, still have an awesome career and maintain a healthy work life balance.–Ben Forrest.
For knowledge workers and those typically in data science, programming, software engineering, artificial intelligence and technology, remote work has given people the flexibility to work from anywhere in the country, presenting new opportunities they might not otherwise have had.
This has led to many knowledge workers buying a home during the pandemic, moving and finding new ways to live where they want, instead of working where they have to work in a designated office. Some companies have even abandoned physical offices altogether.
Less Commute Stress
In studies that say WFM is more productive, commute time is often mentioned as one of the key variables. However, for mental health, commute stress can also be significant for many workers who go to the office. Realistically however, for many remote workers less commute stress may be off-set by technological loneliness, especially among young workers who are single.
Improved mental health is often cited as a reason for a preference for remote work. However not everyone enjoys or tolerates zoom meetings, not in the same way as in-person meetings.
Moving to a More Rural or Scenic Setting
Many remote workers choose to leave large cities and live in more comfortable, scenic and pleasant surroundings.
It seems remote working has more perks than just avoiding a bad commute. On a recent backpacking trip in Oregon and California, I met a woman in a little town called Etna. She used to work in Buffalo, NY, but she moved to California to be near family and continues to support a large IT services company remotely. There’s no way she could have a job like that in a rural town without working from her home.… –Bonnie Nicholls.
Money Savings
With hybrid work or remote work employees report spending less on commuting and on more professional business attire than they would if they were going to the office every single day of the week. Also fewer coffees and other daily expenses mean savings that can add up and be spent on family, entertainment or office supplies at home instead. However this can also be offset by some employers actually reducing salary expenses if employees choose to go full on remote work. This is common if you move from Silicon Valley to somewhere else where the cost of living is cheaper.
More Time With Pets
While more time with loved ones can take many forms, for some Millennials or GenZ who do not have a family yet this means more time with pets which can boost mental health and productivity. Working remotely means you can recharge with micro breaks at home in a way you wouldn’t necessarily do with ear phones on in an open-space office (blast from the past!).
Significant Benefits for Companies Too
While it’s very easy to spot the benefits for the individual employee, what might be the benefits for a company? Some of these include:
Less time spent in meetings and needless office chatter
Higher overall productivity (this has been backed up by numerous studies)
Better engagement
Higher employee retention
Profitability – not having to rent an office is a huge saving.
More Organizational Decentralization (sometimes called Location Independence)
Businesses also function differently when they use remote workers with many reporting better team bonds and less hierarchy.
However location independence is actually having a broader range of potential jobs that aren’t tied to a single geography. This relates to a greater democratization of opportunity and geographically decentralized workforce.
GenZ really prefer the freelancer life and some will not hold down 9 to 5 jobs like we used to, while some of them might even country hop often in a digitally nomadic lifestyle. Of course all of this presumes that you have skills that are translatable to remote work and preferably that you are a knowledge worker that software engineering or data science afford.
Improved Diversity and Inclusion
There’s some preliminary data that suggests remote teams are more inclusive, in that they embrace minorities better and promote more equality in the “workplace”.
Due to location independence, roles can be filled with more expanded criteria. For instance by hiring people from different socioeconomic, geographic, and cultural backgrounds and with different perspectives better teams and products end up being created.
This is a benefit of remote work that could truly impact society in a major way, but more studies are required to back up the initial data.
More Freedom for Employees
Remote work offers workers more freedom that they appreciate so firms that adopt hybrid or remote work conditions may see significantly higher employee retention and higher morale.
Employees report having more freedom to customize their life-work balance the way they want, including making their own schedules. For knowledge work in software development, data science, AI and software engineering this may be an essential requirement, given the diversity among their employees.
This freedom extends to introverts who might be sensitized to the noise and constant interruptions of a physical office. More than anything this perceived freedom implies the company trusts the employee to do their best, even with limited supervision. This kind of trust can mean a pay it forwards reciprocity comes into effect where the remote worker becomes a more positive influence on his team and its productivity.
Better Collaborative Technology
Before the pandemic remote work was something for freelancers and the self-employed. However with remote work becoming the mainstream for knowledge workers, the technology took a sudden leap that empowers better communication, collaboration and support for employees.
The Zoom and Microsoft Teams era among many other tools suggests that these collaboration software services also make for a better support system. The collaboration at a distance approach can mean meetings are shorter, check-ins are more personal and flexibility is higher for all the surprises that life can bring.
That the tech is more collaborative makes remote work easier than it did in the past. Every process has been streamlined, and the way teams function has also been improved with new kinds of digital workforce data. As the hybrid workforce becomes the new normal, this collaborative technology is set to become exponentially better.
Greater Ability to Handle Multiple Projects
Remote work and WFM also allows your side-gigs to prosper or working for several clients at once if you are a freelancer. Remote work enables you to multitask in different ways and as a knowledge worker that could mean your DIY investing, your side-gig, your personal projects and your actual work role.
Remote work gives you the ability to be more productive because you can be yourself in your own customized office space. This ultimately means likely making more money due to the ability to include side projects in your daily activities.
Remote work in this way allows you to fulfill your human potential in a manner that you might not get when stuffed in an office all day long.
Making Friends in the Digital Workforce
Microsoft is creating LinkedIn news promoting remote work and helping knowledge workers navigate it,hereandhere. With Microsoft Teams and their suite of software tools, it’s almost like a product placement. In the ‘AI for good” work making friends at work could also, frankly, be easier. It will no longer be about who you are physically most close to in a physical office setting.
Companies understand in a remote work environment the importance of Slack and making space for social chat. A bonded team is a team that understands itself better and can be more productive. As a result firms are finding out what works to create a friendlier culture for a digital workforce.
During the pandemic when mental health issues were rising, workers created or organized weekly virtual hangouts, which they took offline as people got vaccinated. Some met one-on-one or attended company-wide off-site events like baseball games. As we adapted to lockdowns, we had to find ways to socialize in a way that helped us cope with the various adjustments.
Starting a remote job can be hard, but many peoplehave found new ways to forge work friendshipsduring the pandemic. There’s no longer a proverbial water cooler to generate casual encounters, and some younger workers have never had a physical office at all. But they’ve overcome the awkwardness of the digital chat box to initiate meaningful, if often distanced, friendships.–Krithika Varagur.
For those that don’t like office politics or water-cooler chit chat, remote work actually feels like a blessing. For extraverts, digital software tools make it easier to communicate.
Less Peer-Influenced Overtime Work
While remote workers initially tend to blur the lines between home life and work, as they get used to the new routine they may work less peer influenced overtime. This actually promotes emotional well-being. With some studies suggesting a 4-day work week is most productive, forced overtime is not something knowledge workers should be doing much of.
While some firms, startups and technology companies might expect a significant amount of overtime as part of the job, remote workers have more flexibility with regards to this.
Less forced overtime means improved work-life balance. Many remote workers actually do too much overtime which reduces their job satisfaction. Keeping to a fixed routine helps.
Better Access to Jobs
Remote work can help those who are underemployed or retired bycv 2350/*, working in an easier environment. Some retired knowledge workers get bored and want to continue to actively contribute to society and remote work can afford them that opportunity.
Others who are disabled, those who are caregivers, those who are suffering an illness and others in difficult mental health periods have more flexibility in working remotely. Remote work overall improves accessibility to jobs and professional opportunities.
Sustainable Living Choice
Remote work means less commuting and as such may represent a smaller carbon footprint for your family and you as an individual. Digital transformation technologies might allow major companies to attain carbon neutrality faster.
So by choosing a lifestyle of remote work, you aren’t just saving time and perhaps money, but having a positive environmental impact in your small way. For the U.S. a whopping 7.8 billion vehicle miles aren’t traveled each year for those who work at least part time from home, 3 million tons of greenhouse gases (GHG) are avoided, and oil savings reach $980 million.
So the behavior modification of remote work can have small sustainable environmental impacts. It can also contribute to less traffic congestion, less noise pollution and thus less air pollution in your local region, if remote work becomes a more collective choice in how we approach the future of work as a society.
Sensory Tenderness
For introverts who prefer to work in total silence and in peace and tranquility, working from home has the obvious benefit of a more customized and quiet work space. Busy offices, interruptions, noise, wasteful meetings and crowded conditions create stress that reduces work focus and general feelings of well-being.
From a perspective of too much stimulation, remote work can create a more ideal balance between the physical environment and the preferences of the employee to work in an atmosphere that’s more conducive for them to give it their best, think creatively and come up with novel solutions to work problems.
Greater Access to Talent Pools
For SMBs and firms in general, remote work allows for greater access to a talented pool of workers, no matter where they live in the country of operations or the entire world at large. For particular positions employers want to find the right skills but also the right cultural fit for their organization.
As such, remote work allows HR to improve the talent level of their employees thus benefiting the company and the industry as a whole. For organizations, having an extended reach of talent can make the difference in becoming profitable compared to just scraping by.
Improved Productivity
It’s hard to argue against being more productive. An overwhelming majority of studies demonstrate how remote work allows an organization and the individual to be more productive. As companies adopt remote work for knowledge workers, especially their white collar talent and software engineers, key metrics of productivity and efficiency go up and constructs related to innovation go up.
One of the biggest lessons of the pandemic has been how remote work equals more productivity which might hypothetically eventually lead developed countries to adopt a 4-day work week.
More Immunity from the Great Resignation
With the economic recovery after the pandemic, many employees want to change jobs. Those organizations that are the most pro remote work will retain their best talent. The data might in the end show how remote work means less job hopping for young talent, a key demographic competitive companies need to retain to excel in the future.
Remote work in the future might be one of the necessary requirements to create a successful company culture. With remote work and a more automated HR with AI, companies can become more agile and cost efficient in terms of recruitment and retention of their talent.
Remote work will also allow new data to be used by AI systems that assist in recruiting and on-boarding of employees. This data will ultimately improve the success rate of HR and hiring (talent acquisition) for a company. Remote work will thus likely accelerate how some aspects of HR become automated with chatbots and advanced AI systems.
Improved Internal Communications
As remote work becomes normalized the software systems around collaboration and communication improve and can be tweaked to suit an organization’s needs. This improved communion leads to increased teamwork, loyalty, job satisfaction and productivity. That might be why remote work in the end leads to more productivity in teams and individual performance.
Technology in this way can reduce office politics while augmenting the key metrics of communication with more data on the kinds of communication that matter the most. Remote work is thus an example of how digital transformation software and more knowledge workers going remote might improve a company’s long term prospects.
Conclusion on Adoption of Remote Work
The advent of remote work might lead to a cascade of changes for knowledge workers in a future work revolution never seen before. The arrival of the corporate metaverse might mean significant changes in how we work and navigate work-life balance as a generational shift.
Knowledge workers are in 2021 insisting on having the ability to work hybrid WFM or in some cases take jobs across the country as remote workers. Digital transformation associated with remote work will also create new data science and programming jobs all around the world and change the future of digital collaboration itself.
A large majority of knowledge workers do not want to go back to the office much more than one or two times a week, if at all. Therefore it’s somewhat likely that the benefits of remote work make it a sustainable trends, both for the individual worker and for businesses.
Your Turn on Remote Work?
What do you think?What has been your experience so far with remote work in a world in partial or recurrent lockdowns? Is it something you want implemented in the future for your next job or the future of work itself? Will you insist on remote work as a condition for applying for a position in your field?
This is the first part of a 2-part series on the growing importance of teaching Data and AI literacy to our students. This will be included in a module I am teaching at Menlo College but wanted to share the blog to help validate the content before presenting to my students.
Wow, what an interesting dilemma. Apple plans to introduce new iPhone software that uses artificial intelligence (AI) to churn through the vast collection of photos that people have taken with their iPhones to detect and report child sexual abuse. See the Wall Street article “Apple Plans to Have iPhones Detect Child Pornography, Fueling Priva…” for more details on Apple’s plan.
Apple has a strong history of working to protect its customers’ privacy. It’s iPhone is basically uncrackable which has put it at odds with the US government. For example, the US Attorney General asked Apple to crack their encrypted phones after a December 2019 attack by a Saudi aviation student that killed three people at a Florida Navy base. The Justice Department in 2016 pushed Apple to create a software update that would break the privacy protections of the iPhone to gain access to a phone linked to a dead gunman responsible for a 2015 terrorist attack in San Bernardino, Calif. Time and again, Apple has refused to build tools that break its iPhone’s encryption, saying such software would undermine user privacy.
In fact, Apple has a new commercial where they promote their focus on consumer privacy (Figure 1).
Now, stopping child pornography is certainly be a top society priority, but at what cost to privacy. This is one of those topics where the answer is not black or white. A number of questions arise including:
How much personal privacy is one willing to give up trying to halt this abhorrent behavior?
How much do we trust the organization (Apple in this case) in their use of the data to stop child pornography?
How much do we trust that the results of the analysis won’t get into unethical players’ hands and used for nefarious purposes?
And let’s be sure that we have thoroughly vetted the costs associated with the AI model’s False Positives (accusing an innocent person of child pornography) and False Negatives (missing people who are guilty of child pornography), a topic that I’ll cover in more detail in Part 2.
Data literacy starts by understanding what data is. Data is defined as the facts and statistics collected to describe an entity (height, weight, age, location, origin, etc.) or an event (purchase, sales call, manufacturing, logistics, maintenance, marketing campaign, social media post, call center transaction, etc.). See Figure 2.
Figure2: Visible and Hidden Data from Grocery Store Transaction
But not all data is readily visible to the consumer. For example, from the Point-of-Sales (POS) transaction on the right side of Figure 2, there is data for which the consumer may not be aware that is also captured and/or derived when the POS transaction is merged with the customer loyalty data.
It is the combination of visible and hidden data that organizations (grocery stores in this example) use to identify customer behavioral and performance propensities (an inclination or natural tendency to behave in a particular way) such as:
What products do you prefer? And which ones do you buy in combination?
When and where do you prefer to shop?
How frequently do you use coupons and for what products?
How much does price impact your buying behaviors?
To what marketing treatments do you tend to respond?
Do the combinations of products indicate your life stage?
Do you change your purchase patterns based upon holidays and seasons?
The sorts of customer, product, and operational insights (predicted behavioral and performance propensities) are only limited by the availability of granular, labeled, consumer data and the analysts’ curiosity.
Now there is nothing illegal about the blending of consumer purchase data with other data sources to uncover and codify those consumer insights (preferences, patters, trends, relationships, tendencies, inclinations, associations, etc.). The data these organizations collect is not illegal because you as a consumer have signed away your exclusive right to this engagement data.
There is a growing market of companies that are buying, aggregating, and reselling your personal data. There are at least 121 companies such as Nielsen, Acxiom, Experian, Equifax and CoreLogic whose business model is focused on purchasing, curating, packaging, and selling of your personal data. Unfortunately, most folks have no idea how much data these data aggregators are gathering about YOU (Figure 3)!
Yes, the level of information that a company like Acxiom captures on you and me is staggering. But it is not illegal. You (sometimes unknowingly) agree to share your personal data when you sign up for credit cards and loyalty cards or register for “free” websites, newsletters, podcasts, and mobile apps.
Companies then combine this third-party data with their collection of your personal data (captured through purchases, returns, marketing campaign responses, emails, call center conversations, warranty cards, websites, social media posts, etc.) to create a more complete view of your interests, tendencies, preferences, inclinations, relationships, and associations.
One needs to be aware of nefarious organizations who are capturing data that is not protected by privacy laws. For example, iHandy Ltd distributes the “Brightest Flashlight LED” Android app with over 10 million installs. Unfortunately for consumers, iHandy Ltd is headquartered in Beijing, China where the consumer privacy laws are very lax compared to privacy laws in America, Europe, Australia, and Japan (Figure 4).
Figure4: iHandy Ltd Brightest Flashlight LED Android App
But wait, there’s more. A home digital assistant like Amazon Alexa or Google Assistant and their always-on listening capabilities are listening and capturing EVERYTHING that is being said in your home… ALL THE TIME!
And if you thought that conversational data was private, guess again! Recently, a judge ordered Amazon to hand over recordings from an Echo smart speaker from a home where a double murder occurred. Authorities hope the recordings can provide information that could put the murderer behind bars. If Amazon hands over the private data of its users to law enforcement, it will also be the latest incident to raise serious questions about how much data technology and social medai companies collect about their customers with and without their knowledge, how that data can be used, and what it means for your personal privacy.
Yes, the world envisioned by the movie “Eagle Eye”, with its nefarious, always-listening, AI-powered ARIIA is more real than one might think or wish. And remember that digital media (and the cloud) have long memories. Once you post something, expect that it will be in the digital ecosystem F-O-R-E-V-E-R.
All this effort to capture, align, buy, and integrate all of your personal data is done so that these organizations can more easily influence and manipulate you. Yes, influence and manipulate you.
Companies such as Facebook, Google, Amazon, and countless others leverage your personal propensities, that is, the predicted behavioral propensities gleaned from the aggregation of your personal data, to sell advertising and influence your behaviors. Figure 5 shows how Google leverages your “free” search requests to create a market for advertisers willing to pay to place their products and messages at the top of your search results.
All of your personal data helped Google achieve $147 billion in digital media revenue in 2020. Not a bad financial return for a “free” customer service.
What can one do to protect their data? The first step is awareness of where and how organizations are capturing and exploiting your personal data for their own monetization purposes. Be aware of what data you are sharing via the apps on your phone, the customer loyalty programs to which you belong, and your engagement data on websites and social media. But even then, there will be questionable organizations who will skirt the privacy laws to capture more of your personal data for their own nefarious acts (spam, phishing, identity theft, ransomware, and more).
In Part 2 of this series, will dive into the next aspect of this critical data literacy conversation – AI Literacy and AI Ethics.
When the Covid-19 pandemic first hit, and a vast majority of the population was given the ‘stay at home’ order by Prime Minister Boris Johnson. At the beginning and on the surface, the transition to enforced working from home which begun last year initially (and on its surface) allowed for much-needed flexibility for professional workers who were able to work from home during the COVID-19 crisis and shown that a remote workforce can continue working productively. However, the quieter demons and negative aspects of the experience such as loneliness and low mental health, lack of collaboration, and overall burnout have emerged and continue to emerge as the ‘work from home’ saga goes on.
“The concept of the ‘office’ has been recently shown to actually be a healthy one, which was until recently, fairly unknown. The ‘9 to 5’ working day and separate environment for work allows for a work-life balance,” says Adam Nelson, a writer fromOXEssaysandUKWritings. The separate environment allows for a division between work life and home life that has not been viable during the Covid-19 pandemic. Studies have shown that working from home over the last year has led to longer hours, longer meetings, and spending more time using online and social media communication channels.
In a Los Angeles survey conducted this year, over sixty percent of professionals now working at home due to Covid-19 confessed that they do at least some work almost every weekend. In addition to this, 45 percent of Los Angeles remote workers say they now work more hours during the week than prior to the pandemic. This survey also discovered that parents who now worked remotely were actually more likely to work weekends and longer days than those surveyed without children.
“Though the thought of remote work was expected to provide flexibility to workers during the pandemic, it has in reality made disconnecting from work almost impossible,” explains Ian Jackson, an HR manager atStudydemicandVia Writing. The downside to an entire company’s operation being able to function online and through communication channels such as Zoom and Google Meets, is that the only way to completely avoid work is to turn your phone off (something that rarely happens in our social media charged day and age).
NordVPN Teams, a New York VPN company has noted that, “Remote working has led to a two and a half hour increase in each average working day”. For example, in the UK, employees that would usually leave their jobs at 5 or 6 o’clock at the latest are now logging off their online platforms around 8pm. Remote working has not only led to longer working days, but it has also produced shorter lunch breaks, and a sharp spike in working hours on so-called ‘family holidays’.
Over half of UK employees report working more than the hours that they are technically paid for during remote working, and a staggering 74% report stress, fatigue, or burnout during the so called ‘flexible and relaxing’ work-from-home period. Working at home has led to longer hours, with some employees even allocating their previous commuting time to work as well. Another recent research study discovered that remote workers were undertaking one entire month’s more work per year, compared with before the pandemic.
So, does this mean the death of the office? While work can now be conducted entirely at home, this does not mean it will be productive in the long term. Yes, remote workers are now working longer and more for their employers currently; but the fatigue and burnout reported by current home workers will eventually lead to a massive downfall in productivity. As well as this, the creative exchange of ideas and lack of effective work-life balance has meant that working at home has come at both great personal and professional cost. Working from home this past year has been continuously proven to lead to longer hours, and it will continue to be detrimental to both personal and professional health and development unless we do our best to return to our offices as soon as it is safely possible.
About the Author:
Emily Henry is a professional article writer atBest assignment writing servicesandEssay Help Serviceswho enjoys being involved in multiple all over the world. She is a work-from-home mother; and she enjoys traveling the world, reading and researching management topics and attending business training courses. She also contributes her writing skills atStudentWritingServices.
Marketers are now using data-driven analysis and approaches to utilize accurate research data for their marketing campaigns. Not every data and its digging strategy is relevant and accurate enough to be adopted by a company. The question is how to avoid unauthentic data driving techniques and data blunders.
“Data is the new oil for IT industry”
What does big data comprise?
IMG Source: Researchgate.net
Volume
Velocity
Variety
Veracity
These four V interprets data quality in terms of its quantity, certainty, and categories.
What is the data-driven marketplace and what importance does it hold?
Researching and collecting data for the targeted customers and audience can help in providing accurate services and information. The brand can better understand what itscustomers’ demands and expectationsare. This helps in driving better leads, conversion leads, and successful campaigns.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – By Geoffrey Moore, an American Management Consultant and Author
How can big data mistakes damage the company’s marketing profile?
Wrong data-driven methods can result in a poor response from the target audience with reduced engagements. The marketing team will predict erroneous insights that will damage the prospects of marketing campaigns. A survey was conducted in 2013 for big data evolution, where 81% of the companies mentioned big data strategy as their top five priorities and 55% of them reported failure and implementing big data objectives.
Here’s a golden guide on how to identify and avoid significant data mistakes to keep your marketing game running on fleek.
Ignoring quality-driven data
Researching and accumulating data is not the only requirement as quality overshadows quantity when it comes to customer service. Making the data qualitative in terms of its relevance, confidentiality, and accuracy. The data should be sorted, organized, and cross-checked in all the protocols of data quality control.
“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – By Angela Ahrendts
The fallacious data analytics can develop ineffective insights for marketing campaigns, damaging the perspective and motivation. Infiltrated data can drain the marketing budget into waste by not fulfilling the objective. Take the following precautions:
Follow the taxonomy governance
Avoid using meta tags
Focus on versions
Scan data on regular basis to find potential threats
It’s vital for businesses to plan their marketing budget based on quality and accurate data. Otherwise, they risk missing out on opportunities to grow and expand.
Arranging your database into sub-datasets
The concept of organizing data in a single database in making marketing decisions can help in removing inconsistency. A large data set can average out errors. Small datasets can escalate the chances of inconsistencies in marketing campaigns and can ruin marketing decisions.
To get more accurate insights and to boost the effectiveness of a marketing campaign, the data analyst must focus on building data as relevant and precise as possible.
Source: Ingrammicro.com
From government to energy sectors, every sector is investing to build its big data market. The financial sector has reported owning $6.4B of market value in 2015.
Data with no marketing goal
Researching and analyzing data with no intention is baseless and a waste of money and effort. Data digging has always to be done under some criteria to meet the requirements of target customers. With no specific analysis intention about data, it would definitely result in ambiguity, inaccuracy, and irrelevant insights. The marketing and execution department of the organization focuses on analyzing from a 360 perspective about framing a committed strategy with its all execution phases.
It’s important to analyze metrics to save the budget and efforts of data analysts. In parallel, it’s vital to track the performance of those metrics to see their worth and effect on the marketing campaigns.
The sample data statistics highlight what significance this one-minute data collection shows. The analysis shows 2.5 million Facebook posts were made in 1 minute, showing massive engagement. 72 hours of YouTube videos were uploaded.
How to surmount this?
Before gearing up the analyzing operation, jot down the goals related to data selection and marketing benchmarks. Through this technique, you can use all the resources to find the optimum data set.
Weak or no data architecture plan
Quality, confidentiality, quantity, relevancy, and accuracy make up a solid data set. A database with no framework and plan is like a balloon full of data but no rope tied with it. A structure-less data set can result in discrepancies and ambiguities in performing analysis actions. It will become a menace to store, retrieve, and save the data.
“Data is a precious thing and will last longer than the systems themselves.” – By Tim Berners-Lee, Inventor of the World Wide Web
Enterprises these days have employees and contractors doing data entry work from home because the scale of these entries is too vast. This unorganized data can suffer from constraints and potential threats.The solution lies in building a Data Architecture Plan with a smooth concrete of storage and retrieving. Data-driven enterprises must automate the process of turning signal intelligence into a decision or action, and the way to do this is by creating automated processes powered by AIOps using Robotic Data Automation(RDA). There are many online and offline storage tools such as cloud and edge computing, coupled with low-latency tools.
Improper data visualization
Presentation and proper showcasing of information and raw data come under the art of data visualization. Apart from organizing and storing your database, it’s equally important to make it presentable and visible. It comes under the skills and responsibilities of marketers and data analysts.
A database that fails to deliver the required information has lost the effort and sense of data presentation. Your Data Architecture plan also comprises data allocation and presentation which allows easy conveying of information.
The data should be organized in sections to make it understandable for the audience. The caliber and requirements of the audience should be considered while designing the presentation of data. Haphazardly organized data can result in visual displeasure. You can use software design tools to creatively save and arrange your data with the help of infographics and visuals.
A team lacking the major analytics skills
Most of the data science and mining companies compromise on improving their teams’ skills and expertise according to the upgrade in technology. There are predictive maintenance tools like those that sensors collect to accumulate massive databases in a single space. All the data analysts must be called for regular analysis training to update them with essential tools for developing accurate data insights.Even if you’ve employed freelancers who were looking fortyping jobs from home, you need to arm them with appropriate skills.Most companies and marketers confuse big data by measuring their technical strength of handling it such as storage and computing devices. Whereas, they should be focusing on effective big data initiatives. Once they decide on business strategies for big data, they can allocate supportive technology with it.
“No great marketing decisions have ever been made on qualitative data.” – By John Sculley, CEO of Apple Inc
Zero collaboration between data analysis and business development team
Once your company’s data is set to be cooked for marketing, finance, and business strategies, a lack of a business development sector will bring in no value in the data. A proactive BI team will invest in building its data in terms of its thoughtful resources. They explore and work on driving importance from the collected data for the organization. A Bi team is dedicated to working for the managing, execution, acquisition, and quality-driven utilization of data. Big data requires different forms of treatment of data isolation, management, and authentication which requires some operational procedures.
Comprehend your data set trends
Data collection, analysis, and response show a significant trend for data analysts to understand and work accordingly. When tracking and analyzing the data set, there are various connections in the data that connect different parameters. You can see them as trends or see the interlinks between them.
These trends are not always trustworthy but can help marketers to some extent in some phenomena. The best way to find misleading trends is to look from where the trend comes in. The cause of the trend will signify the credibility of the specific trend.
The bottom line
Massive data analysis and research is a significant sub-department of data marketing strategy. Considering its significance in mind, it has to be error-free as it has a major contribution in developing marketing campaigns and customer-related approaches. To design a flawless data mining procedure for your company, focus on its quality, architecture, and database. As mentioned above, say NO to small data sets as it invites massive inconsistencies.
We can’t argue more on how data has impacted our lives as digital data storage has overpassed 40 zettabytes by the end of 2020. With 70% of the population with their own mobile phone, each individual is contributing some useful data stats to the company, brand, or government. It’s wise enough to say in 2021 that your data commands and control. No one and none of the organizations can control the flow and effect of data. With thousands of copies of a single piece of data all over the world, it’s impossible to control privacy and copyrights.
In the digital era, with every industry getting disrupted by technological advancement it becomes more and more crucial that consumers get a seamless experience for their banking process.
Since the growth in the working population and increase in disposable income are acting as catalysts for boosting the demand for digital banking. In this blog, you will learn about how the role of Artificial Intelligence is helping the banking sector.
Key Takeaways
The growth in the working population and increase in disposable income is acting as a catalyst for boosting the demand for digital banking.
54% of financial services organizations with over 5000+ employees have adopted AI.
Some of the advantages of adopting AI in the Banking sector includes Risk Management, Fraud Detection, enhanced Customer Service Experience, Quick Resolution Time and Digitization.
Adoption of AI in the Banking sector
Artificial Intelligence has taken the banking industry by storm with its offerings and services. Not long ago your nearest vegetable vendor was accepting only cash as a mode of payment. Now you will notice QR scanners for online transactions at almost every shop you visit, which tells us that technology adoption is taking place at a rapid pace due to high internet penetration.
India recorded an internet penetration of 41% this year. According to a report from 2016, a lot of Banks have collaborated with Fintech Startups to provide innovative solutions and a seamless banking experience to their customers.
As per a report published in 2020, the Indian banking sector has a total of 22 private sector banks, 56 regional banks, 46 foreign banks, 96,000 rural cooperative banks and 1485 urban cooperative banks. A majority of the data gets churned out during the process of Back Office Banking Operations.
It refers to the process of managing huge volumes of data and customer databases in order to gain insights that will help in order to help financial institutions function smoothly.
AI is playing a massive role in this domain by assessing the creditworthiness of the borrowers, detecting frauds, money laundering and even enhancing the relationships with the customers.
Types of AI that is being utilized in the Banking sector
Although there are 7 different types of AI, yet Artificial Intelligence can be broadly classified into 2 types, namely:
Weak AI
Strong AI
Weak AI refers to the type of Artificial Intelligence which is primarily used to perform a task to solve a specific problem. These intelligent systems are fed with pre-defined sets of functions to perform a particular task smartly.
Therefore, they are termed as Weak AI or Narrow AI.
Whereas, on the other hand, Strong AI refers to the type of Artificial Intelligence to put it into simple words, the function of a Strong AI is to broadly mimic the human brain.
This means that it has been designed in such a way that it can carry out any task that an actual human being can.
Risk management: One of the most crucial parts of dealing with Banking is Risk Management. Credit Risk Management refers to a situation when the borrower fails to repay the loan and other contractual deals within the stipulated time frame. AI will help with this aspect as Artificial Intelligence will help in tracking mobile banking apps to track and analyze how a user is dealing with their money. This will eventually help the bank to understand the risks associated with sanctioning loans to people and other credit risk management.
Fraud Detection: Every year we are bombarded with news about scams and other financial frauds worth crores of Rupees. In 2020 alone 47% of companies have witnessed fraud. With the technological advancements, even the scammers are finding new ways to get their way through and fool the system. Therefore, to ensure that we reduce the rate of frauds that take place, banking sectors have started using ML-Driven Fraud Analytics. Machine Learning works on the concept of “learning from experience”. By using ML algorithms machines can be trained to find the difference between legitimate and fraudulent transactions. Which will ultimately help in preventing any kind of abnormal activities from taking place.
Seamless Customer Experience: AI in Banking offers a seamless customer experience through Personal Finance with smart features such as chatbots, subscription services and customized notifications. With the help of chatbots, customers can easily be assisted for queries regarding past transactions, spending analysis, savings and finance-related information irrespective of Bank closure or National holidays. This instant dissemination of information will not only help save time but also help the customer to understand the products and services provided by the bank in a better way.
Digitization of the process: With the expansion of internet penetration across the country, digitization of the banking process is becoming a must. One of the biggest challenges that can be resolved through the digitization of the banking process is avoiding the hassle of standing in long queues for getting the bank work done by turning most of the procedures “paperless”. One of the most popular initiatives is the Know Your Customer (KYC)registration process which has become completely online now.
Quick Resolution Time : Artificial Intelligence-powered chatbots are helping the Banking industry in a massive by reducing the operational cost and enhancing the customer experience with quick resolution time. Since now, AI Chatbots function on the concept of “Learning from Experience” which helps in reducing human errors by a huge percentage.
Wrapping Up
In the digital era, “data” is the most important form of currency. With so much of data available everywhere it becomes more important for organizations to make use of it effectively and efficiently.
The technology that is revolutionizing the Fintech industry currency is none other than “Artificial Intelligence”. AI is providing financial institutions with a platform to help people, data and services function coherently.
The role of AI in banking is continuing to gain prominence and the global spending on AI is predicted to touch $300 billion by 2030. While a report published by IBEF estimates thatIndia’s Digital lending would reach US$ 1 Trillion by 2023as it will be driven by a five-fold increase in digital reimbursements.
Quality is the most important criterion by which Google ranks websites in SERP. The more convenient, useful, and interesting a web application is, the more users turn to it. The growing number of visitors makes it clear for the search engine that the website has value and should be raised higher so that it can be viewed by as many people as possible. Being in the TOP 10 in the search results is a paramount task, as statistically, only 0.78% of usersreachthe second page of Google. Therefore, companies should give consideration to not only an SEO audit but also SEO testing because it directly affects the traffic and profits of an organization. Let’s figure out how it works.
What SEO testing is and why it is important
SEO testing is not much different from other types of testing. It involves searching for errors: 404 pages, broken links, irrelevant code, problems with loading pages, visual defects, and so on. All this can be found during UX testing, performance testing, or cross-platform testing. And the goal of SEO testing is to identify problems that can arise after changes in a website before they affect the quality of the web application and organic traffic.
From a tester’s point of view, the 404 error is not a significant bug: it doesn’t cause disruption of the website, and the visitor can go to another page and continue the search. But from the SEO perspective, hundreds of 404 pages can significantly reduce organic traffic. According to Google, 61% of users will leave a website if it has access issues. The search engine sees a lot of low-quality pages with duplicate content and lowers the “intruder” in the ranking.
Here’s another example of the direct impact of errors on traffic. Let’s suppose developers have updated an important page and deleted its heading – H1- by accident. And H1 is one of the mandatory ranking factors, according to which the search engine determines the page content. This is the heading of the page that users see. If H1 is deleted, this important page will simply drop out of the search results, which will lead to a drop in traffic.
From these examples, it becomes clear that any change can lead to the accidental loss of data important for SEO, which will affect the quality of the website and its ranking in the search engine. That’s why testers should devote their attention to SEO. This will allow a business to maintain an unbreakable chain:
No website is immune to problems with SEO. Google has over 200 ranking factors, and the search engine changes its algorithms up to 1000 times a year. You needn’t strive to please it. What’s important is to create a high-quality and user-friendly website and check it after each change in order to provide yourself with the first positions in the search results.
An SEO audit and SEO testing: what’s the difference?
Sometimes non-experts get confused about the difference between an SEO audit and SEO testing. The SEO audit is an analysis of the current state of the website manually or using a special tool. This procedure helps to understand whether the pages are indexed, whether meta tags are written for them, whether the images are optimized, etc. In other words, such research helps to identify content gaps or deficiencies in the information architecture.
SEO testing involves tracking the results after changes to assess their impact or effectiveness. A well-tuned QA process for SEO includes:
Benchmarking testing, where two versions of the source code are compared (intermediate and production);
Testing elements important for SEO (for example, metadata);
Automation(using tools that collect all changes between preparation and production);
Monitoring changes when the application is in production;
An archive of web pages, which contains a history of changes and source code you can return to in the case of a traffic drop.
QA testing helps to identify problems with a website before a product hits the market. The practice of good QA for SEO works as a safety cable, eliminating potential problems and reducing the number of bug fixes.
How often should SEO testing be done?
SEO testing is worth doing as your website is updated. As QA practitioners note, identifying SEO bugs is quite difficult – they may not affect the overall functionality of the website. It takes time for the search engines to re-index the website after bugs are found and fixed. If an error is not instantly eliminated, the website will no longer be included in the first search results.
Fortunately, this is much easier to do, since experts have access to tools that automate the process of collecting data. They identify quality control problems that need to be solved. This makes testers’ jobs much easier, as they no longer need to manually check every page, link, or image.
SEO testing helps to prevent failed migrations, fraudulent redirects, unintentional indexing, disappearing tags, and more. It allows specialists to check important elements for ranking: broken links, missing content, page load speed, missing metadata, and other issues that affect SEO performance.
With so many hands working on a website (developers, designers, project managers, and so on), every new update poses a risk. Since these updates directly affect the sales and success of a business, SEO testing should be an important part of increasing organic search traffic on Google. Therefore, as part of SEO promotion, it is worth tapping into the services of QA specialists who focus on finding defects and improving the quality of software.
In this blog, we shall discuss about how to use H2O to build a few supervised machine learning models. H2O is a Java-based software for data modeling and general computing, with the primary purpose of it being a distributed, parallel, in memory processing engine. It needs to be installed first (instructions) and by default an H2O instance will run onlocalhost:54321. Additionally, one needs to install R/python clients to to communicate with the H2O instance. Every new R / python session first needs to initialize a connection between the python client and the H2O cluster.
The problems to be described in this blog appeared in the exercises / projects in the Coursera course “Practical Machine Learning on H2O,” by H2O. The problem statements / descriptions / steps are taken from the course itself. We shall use the concepts from the course, in order to:
to build a few machine learning / deep learning models using different algorithms (such as Gradient Boosting, Random Forest, Neural Net, Elastic Net GLM etc.),
to review the classic bias-variance tradeoff (overfitting)
for hyper-parameter tuning usingGrid Search
to useAutoML to automatically find a bunch of good performing models
to useStacked Ensemblesof models to improve performance.
Problem 1
In this problem we will create an artificial data set, then run random forest / GBM on it with H2O, to create two supervised models for classification, one that is reasonable and another one that shows clear over-fitting. We will use R client (package) for H2O for this problem.
Let’s first create a data set to predict an employee’s job satisfaction in an organization. Let’s say an employee’s job satisfaction depends on the following factors (there are several other factors in general, but we shall limit us to the following few ones):
work environment
pay
flexibility
relationship with manager
age
set.seed(321) # Let's say an employee's job satisfaction depends on the work environment, pay, flexibility, relationship with manager and age. N <- 1000 # number of samples d <- data.frame(id = 1:N) d$workEnvironment <- sample(1:5, N, replace=TRUE) # on a scale of 1-5, 1 being bad and 5 being good v <- round(rnorm(N, mean=60000, sd=20000)) # 68% are 40-80k v <- pmax(v, 20000) v <- pmin(v, 100000) #table(v) d$pay <- v d$flexibility <- sample(1:5, N, replace=TRUE) # on a scale of 1-5, 1 being bad and 5 being good d$managerRel <- sample(1:5, N, replace=TRUE) # on a scale of 1-5, 1 being bad and 5 being good d$age <- round(runif(N, min=20, max=60)) head(d) # id workEnvironment pay flexibility managerRel age #1 1 2 20000 2 2 21 #2 2 5 75817 1 2 31 #3 3 5 45649 5 3 25 #4 4 1 47157 1 5 55 #5 5 2 69729 2 4 33 #6 6 1 75101 2 2 39 v <- 125 * (d$pay/1000)^2 # e.g., job satisfaction score is proportional to square of pay (hypothetically) v <- v + 250 / log(d$age) # e.g., inversely proportional to log of age v <- v + 5 * d$flexibility v <- v + 200 * d$workEnvironment v <- v + 1000 * d$managerRel^3 v <- v + runif(N, 0, 5000) v <- 100 * (v - 0) / (max(v) - min(v)) # min-max normalization to bring the score in 0-100 d$jobSatScore <- round(v) # Round to nearest integer (percentage)
This is a classification problem. We need to predict “Maker Location.” In other words, using the rating, and the other fields, how accurately we can identify if it is Belgian chocolate, French chocolate, and so on. We shall use python client (library) for H2O for this problem.
Let’s start H2O, load the data set, and split it. By the end of this stage we should have three variables, pointing to three data frames on H2O: train, valid, test. However, if you are choosing to use cross-validation, you will only have two: train and test.
import H2O import pandas as pd import numpy as np import matplotlib.pyplot as plt df = pd.read_csv('http://coursera.h2o.ai/cacao.882.csv') print(df.shape) # (1795, 9) df.head()
As can be seen from the above table, some of the locations have too few records, which will result in poor accuracy of the model to be learnt on after splitting the dataset into train, validation and test datasets. Let’s get rid of the locations that have small number of (< 40) examples in the dataset, to make the results more easily comprehendible, by reducing number of categories in the output variable.
## filter out the countries for which there is < 40 examples present in the dataset loc_gt_40_recs = loc_table[loc_table >= 40].index.tolist() df_sub = df[df['Maker Location'].isin(loc_gt_40_recs)] # now connect to H2O h2o.init() # h2o.clusterStatus()
2. Let’s set x to be the list of columns we shall use to train on, to be the column we shall learn. Here it’s going to be a multi-class classification problem.
ignore_fields = ['Review_Date', 'Bean_Type', 'Maker_Location'] # Specify the response and predictor columns y = 'Maker_Location' # multinomial Classification x = [i for i in train.names if not i in ignore_fields]
3. Let’s now create a baseline deep learning model. It is recommended to use all default settings (remembering to specify either nfolds or validation_frame) for the baseline model.
from h2o.estimators.deeplearning import H2ODeepLearningEstimator model = H2ODeepLearningEstimator() %time model.train(x = x, y = y, training_frame = train, validation_frame = valid) # deeplearning Model Build progress: |██████████████████████████████████████| 100% # Wall time: 6.44 s model.model_performance(train).mean_per_class_error() # 0.05118279569892473 model.model_performance(valid).mean_per_class_error() # 0.26888404593884047 perf_test = model.model_performance(test) print('Mean class error', perf_test.mean_per_class_error()) # Mean class error 0.2149184149184149 print('log loss', perf_test.logloss()) # log loss 0.48864148412056846 print('MSE', perf_test.mse()) # MSE 0.11940531127368789 print('RMSE', perf_test.rmse()) # RMSE 0.3455507361787671 perf_test.hit_ratio_table()
Top-8 Hit Ratios:
k
hit_ratio
1
0.8897638
2
0.9291338
3
0.9527559
4
0.9685039
5
0.9763779
6
0.9921259
7
0.9999999
8
0.9999999
perf_test.confusion_matrix().as_data_frame()
Australia
Belgium
Canada
Ecuador
France
Italy
U.K.
U.S.A.
Error
Rate
0
3.0
0.0
0.0
0.0
0.0
0.0
0.0
2.0
0.400000
2 / 5
1
0.0
2.0
0.0
0.0
0.0
1.0
0.0
0.0
0.333333
1 / 3
2
0.0
0.0
12.0
0.0
0.0
0.0
0.0
1.0
0.076923
1 / 13
3
0.0
0.0
0.0
3.0
0.0
0.0
0.0
0.0
0.000000
0 / 3
4
0.0
0.0
0.0
0.0
8.0
2.0
0.0
1.0
0.272727
3 / 11
5
0.0
0.0
0.0
0.0
0.0
10.0
0.0
0.0
0.000000
0 / 10
6
0.0
0.0
0.0
1.0
0.0
2.0
4.0
4.0
0.636364
7 / 11
7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
71.0
0.000000
0 / 71
8
3.0
2.0
12.0
4.0
8.0
15.0
4.0
79.0
0.110236
14 / 127
model.plot()
4. Now, let’s create a tuned model, that gives superior performance. However we should use no more than 10 times the running time of your baseline model, so again our script should be timing the model.
model_tuned = H2ODeepLearningEstimator(epochs=200, distribution="multinomial", activation="RectifierWithDropout", stopping_rounds=5, stopping_tolerance=0, stopping_metric="logloss", input_dropout_ratio=0.2, l1=1e-5, hidden=[200,200,200]) %time model_tuned.train(x, y, training_frame = train, validation_frame = valid) #deeplearning Model Build progress: |██████████████████████████████████████| 100% #Wall time: 30.8 s model_tuned.model_performance(train).mean_per_class_error() #0.0 model_tuned.model_performance(valid).mean_per_class_error() #0.07696485401964853 perf_test = model_tuned.model_performance(test) print('Mean class error', perf_test.mean_per_class_error()) #Mean class error 0.05909090909090909 print('log loss', perf_test.logloss()) #log loss 0.14153784501504524 print('MSE', perf_test.mse()) #MSE 0.03497231075826773 print('RMSE', perf_test.rmse()) #RMSE 0.18700885208531637 perf_test.hit_ratio_table()
Top-8 Hit Ratios:
k
hit_ratio
1
0.9606299
2
0.984252
3
0.984252
4
0.992126
5
0.992126
6
0.992126
7
1.0
8
1.0
perf_test.confusion_matrix().as_data_frame()
Australia
Belgium
Canada
Ecuador
France
Italy
U.K.
U.S.A.
Error
Rate
0
5.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.000000
0 / 5
1
0.0
3.0
0.0
0.0
0.0
0.0
0.0
0.0
0.000000
0 / 3
2
0.0
0.0
13.0
0.0
0.0
0.0
0.0
0.0
0.000000
0 / 13
3
0.0
0.0
0.0
3.0
0.0
0.0
0.0
0.0
0.000000
0 / 3
4
0.0
0.0
0.0
0.0
11.0
0.0
0.0
0.0
0.000000
0 / 11
5
0.0
0.0
0.0
0.0
1.0
8.0
0.0
1.0
0.200000
2 / 10
6
0.0
0.0
0.0
0.0
0.0
0.0
8.0
3.0
0.272727
3 / 11
7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
71.0
0.000000
0 / 71
8
5.0
3.0
13.0
3.0
12.0
8.0
8.0
75.0
0.039370
5 / 127
model_tuned.plot()
As can be seen from the above plot, theearly-stoppingstrategy stopped the model tooverfit and the model achieves better accruacy on the test dataset..
5. Let’s save both the models, to the local disk, using save_model(), to export the binary version of the model. (Do not export a POJO.)
We may want to include a seed in the model function above to get reproducible results.
Problem 3
Predict Price of a house with Stacked Ensemble model with H2O
The data is available athttp://coursera.h2o.ai/house_data.3487.csv. This is a regression problem. We have to predict the “price” of a house given different feature values. We shall use python client for H2O again for this problem.
The data needs to be split into train and test, using 0.9 for the ratio, and a seed of 123. That should give 19,462 training rows and 2,151 test rows. The target is an RMSE below $123,000.
Let’s start H2O, load the chosen dataset and follow the data manipulation steps. For example, we can split date into year and month columns. We can then optionally combine them into a numeric date column. At the end of this step we shall have train, test, x and y variables, and possibly valid also. The below shows the code snippet to do this.
import h2o import pandas as pd import numpy as np import matplotlib.pyplot as plt import random from time import time h2o.init() url = "http://coursera.h2o.ai/house_data.3487.csv" house_df = h2o.import_file(url, destination_frame = "house_data") # Parse progress: |█████████████████████████████████████████████████████████| 100%
We shall use cross-validation and not a validation dataset.
train, test = house_df.split_frame(ratios=[0.9], destination_frames = ['train', 'test'], seed=123) print("%d/%d" %(train.nrows, test.nrows)) # 19462/2151 ignore_fields = ['id', 'price'] x = [i for i in train.names if not i in ignore_fields] y = 'price'
2. Let’s now train at least four different models on the preprocessed datseet, using at least three different supervised algorithms. Let’s save all the models.
from h2o.estimators.gbm import H2OGradientBoostingEstimator from h2o.estimators.random_forest import H2ORandomForestEstimator from h2o.estimators.glm import H2OGeneralizedLinearEstimator from h2o.estimators.deeplearning import H2ODeepLearningEstimator from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator nfolds = 5 # for cross-validation
Let’s first fit a GLM model. The best performing α hyperparameter value (for controlling L1 vs. L2 regularization) for GLM will be found using GridSearch, as shown in the below code snippet.
As can be seen from above, the deep learning model could achieve the target of RMSE below $123k on test dataset.
3. Finally, let’s train astacked ensembleof the models created in earlier steps. We may need to repeat steps two and three until the best model (which is usually the ensemble model, but does not have to be) has the minimum required performance on the cross-validation dataset. Note: only one model has to achieve the minimum required performance. If multiple models achieve it, so we need to choose the best performing one.
models = [model_GBM.model_id, model_RF.model_id, model_DL.model_id] #model_GLM.model_id, model_SE = H2OStackedEnsembleEstimator(model_id = 'se_gbm_dl_house', base_models=models) %time model_SE.train(x, y, train) #stackedensemble Model Build progress: |███████████████████████████████████| 100% #Wall time: 2.67 s #model_SE.model_performance(test) #ModelMetricsRegressionGLM: stackedensemble #** Reported on test data. ** #MSE: 130916347835.45828 #RMSE: 361823.6418967924 #MAE: 236448.3672215734 #RMSLE: 0.5514878971097109 #R^2: 0.015148783736682492 #Mean Residual Deviance: 130916347835.45828 #Null degrees of freedom: 2150 #Residual degrees of freedom: 2147 #Null deviance: 285935013037402.7 #Residual deviance: 281601064194070.75 #AIC: 61175.193832813566
As can be seen from above, the stacked ensemble model could not reach the required performance, neither on the cross-validation, nor on the test dataset.
4. Now let’s get the performance on the test data of the chosen model/ensemble, and confirm that this also reaches the minimum target on the test data.
Best Model
The model that performs best in terms of mean cross-validation RMSE and RMSE on the test dataset (both of them are below the minimum target $123k) is the gradient boositng model (GBM), which is the Model 2 above.
model_GBM.model_performance(test) #ModelMetricsRegression: gbm #** Reported on test data. ** #MSE: 14243079402.729088 #RMSE: 119344.37315068142 #MAE: 65050.344749203745 #RMSLE: 0.16421689257411975 #Mean Residual Deviance: 14243079402.729088 # save the models h2o.save_model(model_GBM, 'best_model (GBM)') # the final best model h2o.save_model(model_SE, 'SE_model') h2o.save_model(model_GBM, 'GBM_model') h2o.save_model(model_RF, 'RF_model') h2o.save_model(model_GLM, 'GLM_model') h2o.save_model(model_DL, 'DL_model')
A reputed TIOBE index has considered Python as the major and one of the most popular programming languages for web and web app development. It is an extremely powerful, flexible, and advanced language for web design and development.Python development servicesgain ground among entrepreneurs globally for these reasons. Let’s discuss these reasons in this post.
Python has an upper hand over other programming languages when it comes to developing highly functional programming for enterprise websites and web applications. With the addition of various advancements, Python app development can easily meet the complexities and diverse business challenges. Python app developers can take the advantage of the versatility of this language to build efficient web app solutions.
It is sufficient to know the importance of Python that software giants like Google, Facebook, and Microsoft bank on this programming language. Let’s understand why Python is a preferred programming language for web application development. But before digging deep into these reasons, let’s have a brief introduction to Python.
What is Python Language?
It is a highly adaptable and efficient programming language with dynamic typing capabilities. It is useful for developing robust web and web application solutions. As a versatile programming language, Python enables developers to create all sorts of applications including scientific applications, graphics-based system applications, games, command-line utilities, etc. Python consultants can shed light on its usage.
As an open-source programming language, Python offers unrestricted copying, embedding, and distribution of the code. What’s more, Python developers can get all the coding information online with ease. As a result, aPython development companycan come up with flexible and feature-rich web solutions. Python can give enterprises an edge over peers by offering seamless and future-ready solutions.
Python app development is steadily gaining popularity among entrepreneurs who want to integrate advancements of emerging technologies including AI, ML, and IoT. It is possible to bring automation in certain processes with the help of Python-based websites. Companies can hire Python developers to achieve this objective and get success in this challenging time. Let us go through how different web app development domains use this language.
Python Use Cases across Various Web Development Domains
AI (Artificial Intelligence) and ML (Machine Learning)
Python is one of the most preferred programming languages for integrating AI and ML in customized web solutions. It is useful for making the computer ready for ML and assisting AI to analyze large volumes of data. Python-based websites and web applications can easily deal with high web traffic and fetch user data.
Internet of Things (IoT)
Cameras and other in-built tools of the laptop or smartphones can be easily connected to the Internet as and when necessary in Python web applications. Python-powered business websites are capable of managing the existing IoT network when it comes to fetching and sharing valuable data.
Deep Learning
Web applications based on Python support robotics and image recognition. Deep Learning is useful for processing data in a way similar to that of our brain. Python app development services can assist entrepreneurs to bring innovative and intelligent web applications.
Today, hundreds of thousands of developers use Python for web and web app development. A recentStackoverflow surveyhas shown Python as one of the highest in-demand programming languages. Python is a preferred language among developers, and many web developers want to learn it.
Let’s dig deep into the reasons why Python is a preferred language for web and web app development projects.
Top Reasons Why Developers Select Python for Web Development Projects
Python is not a new language. It has been around us since the 90s, but it has evolved in line with the market trends and changing expectations.
Secure Language
When you hire Python developers, you can remain assured of the security and scalability of the web application. A thriving fintech sector prefers Python language for its high security and capability of handling large amounts of data. Senior and experienced Python developers can come up with a functional fintech app with military-level security. Also, developers can find solutions to common issues of Python web development thanks to a thriving community.
Large and Robust Library
There is no exaggeration in mentioning that there is a Python library for everything. Whether entrepreneurs need an elegant website with seamless functionality or a secure and feature-rich web app, the Python library enables developers to build robust web solutions. The world’s most popular Machine Learning (ML) library facilitates Python web developers to integrate machine learning capabilities in the customized web app.SQLAlchemy libraryenables developers to give the power of SQL in the app or website. Python language is capable of enterprise web development patterns containing a simple database with the help of an SQLAlchemy library.
Django Framework
This is one of the biggest reasons for choosing Python for developing complex web applications.Djangois the main web development framework with a highly useful collection of libraries. As a flexible and comprehensive platform for developing any type of web apps, Django can build powerful apps for modern enterprises. You can hire Python Django developers for building user-friendly web apps for your business. Django takes away the pain of the development process and developers can readily focus on demanding tasks instead of basic issues.
Python web development also offers Flask, a polar opposite of Django. Flask is a microframework and has much fewer ready-made parts than Django. However, this platform is not as flexible as Django. Talking about the differences- Django can save the developer’s time whereas Flask requires more time to adapt to changing requirements.
AI and ML Advantages
AI and machine learning technologies are the need of the hour. With Python, you can integrate the functionality of these emerging technologies. This is one of the major reasons for Python’s increasing popularity. It results in a large number of developers who have professional experience in integrating AI-based features into enterprise apps. You can also find many Python developers with ease. In other words, it is much easier tohire Python developersthan to hire C++ or other web developers.
Final Thoughts
When it comes to performance, Python is great. Availability of developers and rich libraries are other big reasons why you should prefer Python for your upcoming web project. A wider talent pool is available for the Python language as compared to other programming languages. You can soon initiate the MVP (Minimum Viable Product) or a big web project using Python.
All you need to do is consult areputed Python development companyor meet experienced Python consultants to build a team quickly and start the development process as soon as possible for your enterprise.
Time is evolving minute by minute and day-by-day. Currently, at this point there is no need to have a great product or service if you want to satisfy your customer requirements or retain the market.
One of the methods to place your business apart from the competition is by supporting innovation and adopting new technologies. That is the reason why organizations bet on digital transformation trying to remain significant and keep up with the market necessities.
With theemerging IoT technologiesin digital transformation, there are various factors that enhance the IoT utility and drive its growth. Data is valuable and AI is making data actionable by supporting digital IoT apps to provide predictive and prospective analytics.
In this article, you will know how IoT affects digital transformation.
What Does Digital Transformation Means For Business?
As indicated by the State of Digital Transformation research, market pressure is the major drive for digital transformation as even well-known market leaders battle to compete with tech-empowered, agile businesses and startups.
Digital transformation is the best way to future-confirm your business and survive during tech disruption.
Along with the customer expectations, more companies are required to change the current business processes (or make totally new ones) with the assistance of technologies, for example leaving on the path of digital transformation.
From the customer experience that is offered to how you handle your internal processes, digital transformation significantly affects all parts of your business, both internal and external.
Advantages Of Digital Transformation
Improves customer experience
Providing digital and advanced tools to the customers assists with making their lives simple and easy. It makes the business more appealing to potential customers. Organizations that offer obsolete tools and technologies will experience trouble competing with those who utilize new and updated technologies.
Empowers data-driven decision-making
Digital Transformation enables organizations to carry out data-driven management by utilizing an assortment of tools for tracking metrics and data analysis. This, thus, assists in providing a better outcome and improving supply chain performance.
Improved efficiency
Inventive programming tools for process automation leads to further developed proficiency, which thus, brings about cost savings and decreases friction in the business.
Greater security
By changing to modern software frameworks, organizations can secure their data in a better way. Today, customers are very much aware of data security issues, so this is the best method to win their trust and loyalty.
How Does The Internet Of Things Affect Digital Transformation?
There are numerous startups, whose entire business model is developed around the IoT product line. However, traditional organizations across various different spaces can likewise profit by introducing emerging IoT technology solutions to fuel their established business measures.
There are various ways you can transform your business using IoT. Here are some of the methods in which IoT is driving digital transformation and increasing the demand for IoT App development:
Starting new business opportunities
By utilizing information generated by IoT devices, organizations can better understand their customers’ requirements and change their product offerings likewise and also present new products or services to cater to a more vast crowd.
By profiting on new sources of consumer information, for example with IoT devices, organizations can acquire deep knowledge into the customer behavior and tailor their customer experience accordingly – through cutting edge personalization and increased availability.
Boosting business efficiency
Merging rich data experiences with autonomous sensors, the internet of things mobile applications has the potential to build business productivity through process automation. There are many eminent processes that can be streamlined, including stock management, logistic management, security, energy maintenance, and so on.
Reducing operating costs
Process automation will inevitably lead to cost savings and will allow you to use resources in a wise manner. For instance, IoT energy solutions can assist you with managing utility consumption and disposal of waste. This methodology can be applied to warming, ventilation and air conditioning systems, lighting, water supply, and so on.
Improving employee productivity
Very much like cloud and mobile technology advancements, IoT can assist you with engaging your staff, offering better dexterity and making your business system accessible anytime and anywhere. Smart sensors can keep employees connected all time and convey real-time experiences for better productivity.
Bottom Line
The emerging IoT technology has led businesses to work in smart ways by connecting devices and placing real-time information to customers and employees, to provide a personalized and satisfying experience. With IoT transformation, there is secure integration into business processes and workflow.
It is advised that organizations should get their technology stack in place to brace the impact that new technologies like IoT and Digital transformation will bring.
