Do we need AutoML… or AutoDM (Automated Data Management)?

Instead of focusing on “Automated Machine Learning” or AutoML, maybe we should focus on “Automated Data Management” or AutoDM?

You probably know that feeling. You start a blog with some ideas to share, but everything changes once you get started. That’s what happened with this blog.  I discussed the promise and potential of Automated Machine Learning (AutoML) in my blog “What Movies Can Teach Us About Prospering in an AI World“.  It seems quite impressive.

So, I decided to conduct a LinkedIn poll to garner insights from real-world practitioners, folks who can see through the hype and BS (and that’s not Bill Schmarzo…or maybe it should be) about the potential ramifications of AutoML.  It was those conversations that lead to my epiphany. But before I dive into my epiphany, let’s provide more on AutoML.

“Automated machine learning (AutoML) automates applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw data to the deployable machine learning model. The high degree of automation in AutoML allows non-experts to create ML models without being experts in machine learning. AutoML offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.[1]” See Figure 1.

Figure 1: Image sourced from: “A Review of Azure Automated Machine Learning (AutoML)”

Man, that is quite a promise. But here’s the AutoML gotcha: to make AutoML work, data experts need to perform significant data management work before getting into the algorithm selection and hyperparameter optimization benefits of AutoML.  This includes:

• Data Pre-processing which includes data cleansing (detecting and correcting corrupt or inaccurate records), data editing (detecting and handling errors in the data), and data reduction (elimination of redundant data elements).
• Data wrangling which transforms and maps data from one “raw” data format into a format that is usable by the AI/ML models.
• Feature Engineering which is the process of leveraging domain knowledge to identify and extract features (characteristics, properties, attributes) from raw data that are applicable to the problem being addressed.
• Feature Extraction involves reducing the number of features or variables required to describe a large set of data. This likely requires domain knowledge to identify those features most relevant to the problem being addressed.
• Feature Selection is the process of selecting a subset of relevant features (data variables) for use in model construction. Again, this likely requires domain knowledge to identify those features most relevant to the problem being addressed.

That’s a lot of work to do before even getting into the AutoML space.  But us old data dogs already knew that 80% of the analytics work was in data preparation.  It’s just that today’s AI/ML generation needs to hear that, and who better to deliver that message than one of the industry’s AI/ML spiritual leaders – Andrew Ng.

Here is a must watch video from by Andrew titled “Big Data to Good Data: Andrew Ng Urges ML Community to Be More Data…”.  There are lots of great insights in the video, but what struck me was Andrew’s own epiphany on the critical importance of spending less time tweaking the AI/ML models (algorithms) and investing more time on improving the data quality and completeness that feeds the AI/ML models. Andrew’s message is quite clear:  while tweaking the AI/ML algorithms will help, bigger improvements in overall AI/ML model performance and accuracy can be achieved by quality and completeness improvements in the data that feed the AI/ML algorithms (see Figure 2).

Figure 2: Transitioning from Algorithm-centric to Data-centric AI/ML Model Development

And note that those improvements in data quality and completeness that feeds the AI/ML models will benefit all AI/ML models that use that same data!  Sounds a lot like the Schmarzo Economic Digital Asset Valuation Theorem – the economic theorem on sharing, reusing, and refining of the organization’s data and analytic assets.

In the video, Andrew shared hard data with respect to improvement in results from tweaking the model (algorithm) versus improving data quality and completeness (see Figure 3).

Figure 3: Improving the Code versus Improving the Data

In the three use cases in Figure 4, there was literally no improvement in AI/ML model accuracy and effectiveness from tweaking the AI/ML models.  However, efforts applied against improving the data yielded quantifiable improvements, and in one case, very significant improvements!

Figure 4 shows the LinkedIn poll results where I asked participants to select the option they felt was most true about AutoML (sorry, only 4 options are available on LinkedIn).

Figure 4: LinkedIn AutoML Poll

If we factor the “All of the Above” choice with the top two choices, we get the following results:

• 62% of respondents feel AutoML will help automate data science model development
• 56% of respondents feel AutoML will enable business users to build their own ML models

Unfortunately, not having a “None of the Above” option was unfair because the results of the poll differ from poll comments. Here is my summary of those comments:

• AutoML will not be replacing data scientists anytime soon. However, AutoML can help jumpstart the Data Science process in ML model exploration, model selection, and hyperparameter tuning.
• AutoML will not suddenly turn business analysts into data scientists. That’s because ~80% of the ML model development effort is still focused on data preparation. To quote one person, “AutoML by untrained users would be like giving an elite athlete training plan and diet to average people and expecting elite results.”
• AutoML will be even more lacking as Data Scientist’s data preparation work evolves to semi-structured (log files) and unstructured data (text, images, audio, video, smell, waves).
• Realizing the AutoML promise will require a strong metadata strategy and plan.
• AutoML could help in AI/ML product management as the number of production ML models grows into the hundreds and thousands. But AutoML would need an automated set-up to monitor and correct for ML data drift while in production.
• Automating the ML process is just a small step. AutoML results need to be explainable to help in the evaluation of the analytic results using techniques such as SHAP or CDD.
• AutoML is a commodification of the loops and utilities that ML folks run through various ML algorithms, tune hyper-parameters, create features, and calculate metrics of all kinds.
• AutoML can be a great tool to get align teams around an organization’s ML aspirations. A field only flourishes when everyone from every discipline can use it to try different ideas.
• For AutoML to be successful, it is critical important to scope, validate, and plan the operationalization of the problem that one is trying to solve (e.g., Is the target variable here *really* what you want to model? Are all of the inputs available in a production environment? What decisions will this model support? How will you monitor the ongoing accuracy and usage of the model? How will you govern changes to the model, including commissioning and decommissioning it?). Hint, see Hypothesis Development Canvas?
• Finally, is AutoML a marketing ploy by cloud vendors to broaden their appeal to include enabling business users to build their own ML models?

I suggest that you check out the chat stream.  The comments were very enlightening.

My takeaway is that the concept of AutoML is good, but scope of the AutoML vision is missing 80% of the AI/ML model development and operationalization – providing high quality and complete data that feeds the AI/ML models. Figure 5 from “Big Data to Good Data: Andrew Ng Urges ML Community to Be More Data…” nicely summarizes the broader AutoML challenge with respect to data management.

Figure 5: Scope of What AutoML Needs to Address

Instead of focusing on “Automated Machine Learning” or AutoML, maybe we should focus on “Automated Data Management” or AutoDM?

Now that’s a thought…

[1] Wikipedia, AutoML https://en.wikipedia.org/wiki/Automated_machine_learning

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Eight Tips to Manage Your Remote Team in 2021

The concept of remote work was alien to almost every professional until March 2020 when the World Health Organization declared COVID-19 a deadly pandemic.

After the severity of the situation increased, every organization, every professional had to adapt to remote working mandatorily as the governments across the world announced complete lockdowns and major restrictions on the movement.

With no prior experience and understanding of how remote work is carried out at scale, organizations, business leaders, HR leaders, senior and mid-level managers, as well as executive-level staff struggled to find the right spot between communication, executive, and innovation.

The first half of 2020 was a learning curve for everyone while they accepted and understood the work from home reality, and adapted accordingly. But 2021 is definitely the year where new norms of working are getting defined and remote working is going to play a really big part. According to a survey done by Tecla, 85% of managers believe that teams with remote work will become the future of work.

“The future we envision for work allows for infinite virtual workspaces that will unlock social and economic opportunities for people regardless of barriers like physical location. It will take time to get there, and we continue to build toward this.” – Andrew Bosworth, VP Facebook Reality Labs

Since remote work is the future, every manager should know the nuances, best practices, and strategies of managing a remote team which is what we are going to discuss in this article.

[Tried & Tested] Tips & Strategies to Manage a Remote Team in 2021

All the strategies and tips listed below to manage a remote team are based on the biggest struggles of working remotely.

[Source: Buffer]

Communicate All the Kinks Out

It’s easier to communicate but it’s the hardest to communicate clearly and effectively. The better the communication, the easier it will be for you to set expectations, communicate deadlines and project specifics.

Use deliberate and structured communication whenever communicating with your team members. If possible, have weekly work meetings on Mondays to set goals for the week and then a sort of debriefing and fun meetings on Fridays for catching up, understanding progress on projects, weekend plans, etc. This way, you hit the sweet spot between formal, productive, and informal communication.

Also, the key to successfully conducting these meetings is video calls, so always make sure your team is switching their videos on and proactively participating in conversations. To be more effective, invest time in your team members – have a habit of conducting 1-1 meetings with them on a regular basis to understand how they are doing.

“Technology now allows people to connect anytime, anywhere, to anyone in the world, from almost any device. This is dramatically changing the way people work, facilitating 24/7 collaboration with colleagues who are dispersed across time zones, countries, and continents. ” — Michael Dell, Dell

Establish Complete Feedback Loops

Having feedback loops is quite important for remote teams since everyone is working remotely and in different time zones.

Establishing a feedback culture will help you provide support to your team members at an individual level, identify pain areas in operations, stay ahead of any potential conflict, and build meaningful relationships. All this will help improve your team’s overall performance.

Tips on How to Build Feedback Culture

• Make it a part of your process from day 1
• Create a safe environment for your team to express their feedback and concerns openly
• Train your team to give receive feedback – it is essentially a skill
• Use different feedback channels like 1-on-1 meetings, 360 feedback, anonymous feedback, etc.

Boundaries are Productive

After working from home for over a year, all of us have realized that the lines between work life and personal life can easily get blurred when you are working from home. So, it’s important to set some healthy boundaries for all your remote team members to avoid extra, unnecessary stress, and burnout.

For example, recently, Bumble – a dating app company, announced a week-long holiday for all their employees to avoid burnout.

A Few Ways to Go About Setting Boundaries

• Limit availability
• Ask them to avoid connecting their professional accounts on their personal devices
• Encourage wellness and self-care activities like mindful meditation breaks
• Share about personal interests and hobbies or any other non-work talks to keep it light
• Most importantly, don’t schedule too many meetings

Invest in Right Tools & Technologies

Since all of your team members are scattered across different cities, countries, and even continents, it becomes imperative to invest in the right tools and technologies that enable effective timely collaboration.

Things to Keep in Mind While Choosing Tools for Your Team

• Consider all the use cases and then hunt for the right product.
  

Choose future-proof tools that enable digital transformation for your organization. For example, if you were planning to invest in SaaS tools that enhance customer experience, then instead of going for software that enables better communication between your team and customers, invest in software that adds to their experience directly like a product tour software for customers’ onboarding. 

Here are some product tour examples and a guide on how to make the most out of such software.

• Review your process and needs and then choose accordingly
• Do your research, thoroughly
• Get the tools customized, if needed
• Pick the tools that allow you to create an integrated ecosystem
• Train your team

“The whole conversation is about how remote work is different, instead of being about the amazing tools we have at our disposal that remote teams and non-remote teams are able to use at any time. We have this opportunity to have a lot more freedom in our environment compared to when we had to be in an office, or even in school, 40 hours per week.” — Hiten Shah, FYI

Mandatory Monthly or Quarterly Holidays

If your team members haven’t opted for any holidays in the last few months, then make sure they take holidays, mandatorily. Just because we all are working from home and can’t travel, for the time being, doesn’t mean we don’t need holidays.

So make sure to keep an eye out for your team by encouraging them to time off even if they think they don’t need it.

Lastly, Acknowledge & Celebrate Milestones and Hard Work

Celebrating achievements and milestones is quite necessary to keep everyone motivated. Before remote working became the norm, it used to be easy to gather everyone and celebrate individual achievements, company-wide milestones. But when everyone is working from their own spaces, celebrating achievements gets forgotten easily. 

So, make sure to make it a habit to celebrate and acknowledge your team members whenever they are due.

“Now that companies have built the framework – and experienced the cost and time savings associated with it – there’s no real reason to turn back.”  –  Mark Lobosco

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Charticulator : Creating Interactive Charts Without Code

Background

Many areas of AI continue to show innovation at an exciting pace.

For example, today, generative methods are on my radar. So, its nice to see innovation in an area which is stable and mature.

Charticulator from Microsoft research is a free and open source tool for creating bespoke Interactive Chart designs without the need for coding

A gallery of charticulator charts gives you an example of what’s possible

What is the innovation?

While its visually interesting, its important to understand what is the innovation here

Charticulator is an interactive authoring tool that allows you to make custom and reusable chart layouts.

Typically, chart creation interfaces force authors to select from predetermined chart layouts, preventing the creation of new charts.

Charticulator, on the other hand, converts a chart specification into mathematical layout constraints and uses a constraint-solving algorithm to automatically compute a set of layout attributes to actualize the chart.

It enables the articulation of complex marks or glyphs and the structure between these glyphs, without the need for any coding or constraint satisfaction knowledge.

The capacity to build a highly customized visual representation of data, tuned to the specifics of the insights to be given, increases the chances that these insights will be recognized, understood and remembered in the final implementation.

This expressiveness also provides a competitive advantage to the creator of this visual representation in a landscape saturated with traditional charts and graphs.

The charticulator approach lies at the intersection of three ideas:

• People make charts by hand drawing or programming, in addition to using interactive charting tools. Because they cannot tie many aspects of data to graphical elements, illustration tools are insufficient for building custom charts.
• On the other hand, most interactive charting tools based on templates require chart authors to first select from a set of basic chart types or templates, such as bar, line, or pie charts and offer limited customization possibilities beyond that.
• Meanwhile, creating a powerful custom chart with a library like D3.js or a declarative language like Vega gives you a lot of control over how data is encoded to graphical markings and how they are laid out. However, this method is only available to a restricted set of people with advanced programming skills.

So, you could perhaps think that the innovation of charticulator lies in democratizing the custom chart approach and making it easier with no code / low code – and therein lies the innovation IMHO

From a research standpoint, the following are the work’s significant research contributions:

• Charticulator’s design framework, which may be used to create a variety of reusable chart layouts.
• The implementation of Charticulator, a design framework that achieves the design framework by transforming the chart specification into layout constraints and incorporates a constraint-based layout algorithm and a user interface that allows interactive chart layout definition.
• The results of three types of testing: a gallery of charts to demonstrate Charticulator’s expressiveness, a chart reproduction study, and a click-count comparison versus three current tools

I think this is an exciting innovation – and also great that it is free and open source

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Big Data: Effective tips to success

                                                              Image source: Data Science Blog

Can data especially big data be considered as the new gold? Considering the pace at which data is evolving all across the globe, there is little question. Big data contains huge information and we can extract them by performing big data analysis. Consider the following: 

• Netflix saves $1 billion per year on customer retention only by utilizing big data.
• Being the highest shareholder of the search engines market, Google faces 1.2 trillion searches every year, with more than 40,000 search queries every second!
• Additionally, among all the google searches. 15% of those are new and are never typed before, leading to the fact that a new set of data is generated by Google continuously regularly. The main agenda is to convert data into information and then convert that information into insights. 

Why need a Proper Big Data Analysis Strategy?

Organizations were storing tons of their data into their databases without knowing what to do with that data until big data analysis became a completely developed idea. Poor data quality can cost businesses from $9.7 billion to 14.2 millions every year. Moreover, poor data quality can surely lead to wrong business strategies or poor decision-making. This also results in low productivity and sabotages the relationship between customers and the organization, causing the organization to lose its reputation in the market.  

To deter this problem, here is a list of five things an enterprise must acquire in order to turn their big data into a big success:

Strong Leadership Driving Big Data Analysis Initiatives  

The most important factor for nurturing data-driven decision-making culture is proper leadership. Organizations must have well-defined leadership roles for big data analytics to boost the successful implementation of big data initiatives. Necessary stewardship is crucial for organizations for making big data analytics an integral part of regular business operations. 

Leadership-driven big data initiatives assist organizations in making their big data commercially viable. Unfortunately, only 34% of the organizations have appointed a chief data officer to handle the implementation of big data initiatives. A pioneer in the utilization of big data in the United States’s banking industry, Bank of America, specified a Chief Data Officer (CDO) who is responsible for all the data management standards and policies, simplification of It tools and infrastructures that are required for the implementation, and setting up the big data platform of the bank. 

Invest in Appropriate Skills Before Technology

Having the right skills are crucial even before the technology has been implemented: 

• Utilize disparate open-source software for the integration and analysis of both structured and unstructured data. 
• Framing and asking appropriate business questions with a crystal-clean line of sight such as how the insights will be utilized, and 
• Bringing the appropriate statistical tools to bear on data for performing predictive analytics and generating forward-looking insights. 

All of the above-mentioned skills can be proactively developed for both hiring and training. It is essential to search for those senior leaders within the organization who not only believe in the power of big data but are also willing to take risks and perform experimentation. Such leaders play a vital role in driving swift acquisitions and the success of data applications. 

Perform Experimentation With Big Data Pilots

Start with the identification of the most critical problems of the business and how big data serves as the solution to that problem. After the identification of the problem, bring numerous aspects of big data into the laboratory where these pilots can be run before making any major investment in the technology.  Such pilot programs provide an enormous collection of big data tools and expertise that prove value effectively for the organization without making any hefty investments in IT costs or talent. By working with such pilots, implementation of these efforts at a grassroots level can be done with minimal investments in the technology. 

Search For a Needle in an Unstructured Hay 

The thing that always remains on the top of the mind of businesses is unstructured and semistructured data – the information contained in documents, spreadsheets, and similar non-traditional data sources. According to Gartner, data of organizations will evolve by 800% in the upcoming five years and 80% of that data will be unstructured. There are three crucial principles associated with unstructured data. 

• Having the appropriate technology is essential for storing and analyzing unstructured data. 
• Prioritizing such unstructured data that is rich in information value and sentiments. 
• Extracting relevant signals must be done from the insights and must be combined with structured data for boosting business predictions and insights.

Incorporate Operational Analytics Engines

 One potential advantage that can be attained by using big data is the capability of tailoring experiences to customers based on their most up-to-the-minute behavior. Businesses can no longer extract the data of last month, analyze that data offline for two months, and act upon the analysis three months later for making big data a competitive benefit.

Take, as an example, loyal customers who enter promotional codes at the time of checkout but discover that their discount is not applied to result in a poor customer experience.

Businesses need to shift their mindset of traditional offline analytics to tech-powered analytic engines that empower businesses with real-time and near-time decision-making, acquiring a measured test and learn approach. This can be achieved by making 20% of the organization’s decisions with tech-powered analytical engines and then gradually increasing the percentage of decisions processed in this way over time as comfort grows about the process. 

Final Thoughts 

In this tech-oriented world and digitally powered economy, big data analytics plays a vital role in the proper navigation of the market and to come up with appropriate predictions as well as decisions. Organizations must never ignore understanding patterns and deterring flows. especially as enterprises deal with different types of data each day, in different sizes, shapes, and forms. The market of big data analytics is growing dramatically and will reach up to $62.10 billion by the year 2025. Considering that progression, 97.2% of the organizations are already investing in artificial intelligence as well as big data. Hence organizations must acquire appropriate measures and keep in mind all the crucial above-mentioned tips for turning their big data into big success to stay competitive in this ever-changing world.

Source..

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Key Attributes in ER Diagrams

• Overview of different types of keys used in E-R diagrams.
• How to establish a primary key from a set of alternatives.
• Composite, superkey, candidate, primary and alternate keys explained.
• Other keys you may come across include foreign and partial keys.

If you’re unfamiliar with entities and attributes, you may want to read Intro to the E-R Diagram first.

The ER diagram is a way to model a database in an organized and efficient way. A key is a way to categorize attributes in an E-R diagram. When you first start making E-R diagrams, the number of different choices for keys can be overwhelming. However, the goal of the E-R diagram is to create a simplified “bird’s eye” view of your data. A judicious choice of keys helps to achieve that goal. Although there are many different types of keys to choose from in a set of data, relatively few will actually make it to your finished diagram.

Composite Keys

In general, keys can be single-attribute (unary) or multi-attribute (n-ary). A composite key requires more than one attribute. If a key is composite, like {state,driver license#}, a composite attribute can be made from the separate parts. In the early stages of database design, like E-R diagram creation, entity attributes are often composite or multi-valued. However, these may create problems down the road and need to be handled in specific ways when you translate into an actual database [1]. 

Superkey, Candidate, Primary and Alternate Key

 A superkey of an entity set is an attribute, or set of attributes, with values that uniquely identify each entity in the set. For example, a DMV database might contain the following information:

In this example, {License #} is a superkey as it is a unique identifier. {Make,Model,Owner,State,License#,VIN#} and {State,License#,VIN#} are also superkeys. On the other hand, {Owner} or {Make,Model,Owner} are not superkeys as these could refer to more than one person [2].

A candidate key is a minimal super key that uniquely identifies an entity. Minimal superkeys have no unnecessary attributes; In other words, superkeys that don’t have subsets that are also superkeys.  For example, {State,License#} or {VIN} in the above set are possible choices for candidate keys.  

One you have identified all the candidate keys, choose a primary key. Each strong entity in an E-R diagram has a primary key. You may have several candidate keys to choose from. In general, choose a simple key over a composite one. In addition, make sure that the primary key has the following properties [3]:

1. A non-null value for each instance of the entity.
2. A unique value for each instance of an entity.
3. A non-changing value for the life of each entity instance.

In this example, the best choice to identify a particular car is {VIN}, as it would never change for the lifetime of the vehicle. The first digit of a driver license number will change when a name change occurs, so this does meet the requirements of property 3 above. In addition, {VIN} is the logical choice because it is directly associated with the car. If an ownership change would occur, the VIN would stay the same. An alternate key is any candidate key not chosen as the primary key. For this example, {State,License#} is an alternate key. A partial key identifies a weak entity. Weak entities–those that rely on other entities–do not have primary keys [4]. Instead, they have a partial key–one or more attributes that uniquely identify it via an owner entity. 

When the word “key” is used in an E-R diagram, it usually refers to the primary key for an entity [5]. Show the primary key by underlining the attribute. 

Dashed underlines indicate partial keys. 

Other Keys You May Come Across

Foreign keys are not used in E-R models, but they are used in relational databases to indicate an attribute that is the primary key of another table. Foreign keys are used to establish a relationship when both tables have the same attribute [5].

A secondary key is used strictly for retrieval purposes and accessing records [5]. These keys do not have to be unique and are typically not included in an E-R diagram. The term secondary key is also occasionally used as a synonym for alternate key [6].

References

[1] Entity-Relationship modeling

[2] Relational Model and the Entity-Relationship (ER) Model

[3] Primary and Foreign Keys

[4] Entity-Relationship Diagram Symbols and Notation

[5] The Entity-Relationship Model

[6] Database Design ER

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How Data Science And Machine Learning Works To Counter Cyber Attacks

We are all aware of the heinous cyber-attack that took down more than 200,000 systems in 150 countries in only a few days in May 2017. This was found by the National Security Agency (NSA) and was nicknamed “WannaCry,” which exploited a vulnerability and stole important resources before being distributed online. 

After successfully accessing the computer, it encrypted the machine’s contents and rendered them unreadable. Now, victims of the assault were informed they needed to acquire special decryption software to retrieve their stolen material. Furthermore, the attackers marketed this software.

This ransomware outbreak targeted both people and big organizations, including the United Kingdom’s National Health Business, Russian banks, Chinese schools, the Spanish telecommunications company Telefonica, and the US-based transportation service FedEx. 

The overall losses were estimated at $4 billion. Other forms of cyber intrusions, such as crypto jacking, which are more subtle and less destructive but costly, are on the rise. Even high-profile firms with sophisticated cybersecurity processes are vulnerable. 

A recent panic at Tesla in 2018 was averted owing to a diligent third-party team of cybersecurity specialists. As a result, there were over 11 billion malware infections in 2018. That is a major problem that cannot be solved solely by humans.

Fortunately, this is where machine learning may come in handy.

How Machine Learning Helps to Boost Cybersecurity? 

Machine learning is a subset of artificial intelligence that makes assumptions about a computer’s behavior by using algorithms from prior datasets and statistical analysis. It allows the computer to modify its operations and even execute functions for which it was not expressly intended. Thus, the role of ML and AI in cybersecurity has been increasing. 

Machine learning is increasing in popularity to detect risks and automatically eliminate them before they can wreak mayhem. It can filter through millions of files and detect potentially dangerous ones. This was accomplished by Microsoft’s software in early 2018.

According to the firm, hackers utilized Trojan spyware to infiltrate hundreds of thousands of systems and run rogue cryptocurrency miners. Microsoft’s Windows Defender, a software that utilizes many layers of machine learning to identify and block potential threats, effectively blocked this attack. 

As a result, the business was able to shut off the crypto miners as soon as they began digging. Machine learning is used to search for network vulnerabilities and automate actions, in addition to detecting early threats. Machine learning excels at some tasks, such as swiftly scanning vast volumes of data and evaluating it with statistics. Cybersecurity systems create massive amounts of data, so it’s no surprise that this technology is so beneficial. As a result, in the domain of cybersecurity, this is proving to be a big benefit.

Microsoft, Chronicle, Splunk, Sqrrl, BlackBerry, Demisto, and other big corporations are utilizing machine learning to strengthen their cybersecurity systems.

How Modern Data Science Powered by AI Identifies and FIxed IT Vulnerabilities

Here is how data science helps identify and resolve IT vulnerabilities:

1- Improve the Usage of Technologies

Modern Data Science has the potential to both improve and simplify the usage of such technologies. A machine-learning algorithm may be fed both current and historical data through data science. So that the system can detect possible problems accurately over time.

This allows the system to be more precise since it can predict assaults and identify potential vulnerabilities. 

2- Use Encryption

A data breach or assault can cause severe damage to your organization in terms of the loss of important data and information.

This is where data science comes in handy since it uses very sophisticated signatures or encryption to prevent anyone from delving into a dataset. 

3- Create Protocols

Data science has the potential to create impenetrable protocols. By examining the history of your cyber-attacks, you may create algorithms to detect the most often targeted pieces of data. Data science programs may assist you in harnessing the potential of data science to empower networks powered by self-improving algorithms.

Why Should Companies Hire Qualified Professionals?

Thus, the above points indicate the importance of data science and qualified data science professionals in your firm. Focus on hiring professionals who have a master’s degree in engineering in data science and the knowledge of how to decode big data.

We have access to a massive amount of data, and the data is typically telling a narrative. You should be able to identify deviations from the norm if you understand how to analyze data. and such variations can occasionally signal a threat. And, owing to the usage and advancements achieved in machine learning, dangers may now be appropriately countered in a wide range of industries. It is used for image recognition and speech recognition applications.

Even though cybersecurity has improved as a result of this process, humans remain critical. Some individuals believe that you can learn everything from data, but this is just not true. An over-reliance on AI might lead to a false sense of security. 

However, without a doubt, artificial intelligence will become increasingly widespread in maintaining security. It’s maturing, and it’s a feature, not a business. It will play a part in resolving a certain issue. 

Final Thoughts

However, AI cannot address every problem. It will be a tool in the toolbox. At the end of the day, humans are the overlords. 

As a result, in addition to carefully deployed algorithms, cybersecurity specialists, data scientists, and psychologists will play an important role. Human efforts, like those of all existing artificial intelligence and machine learning supplements, augment rather than replace them.

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What is data preparation?

Good data preparation gives efficient analysis, limits errors and inaccuracies that can occur to data during processing, and makes all processed data more accessible to users. It has also gotten easier with the self-service data preparation tool that enables users to cleanse and qualify on their own.

Data preparation:

In simple terms, data collection can be termed as collecting, cleaning, and consolidating data into one file or data table, primarily for use in the analysis. In more technical terms, it can be termed as the process of gathering, combining, structuring, and organizing data to be used in business intelligence (BI), analytics, and data visualization applications. Data preparation is also referred to as data prep.

Importance of data preparation

Fix errors quickly – Data Preparation process helps to catch errors before processing. After data has been removed from its source, these errors become more challenging to understand and correct.

Top-quality data – Data Cleansing and reformatting datasets ensure that all data used in the analysis will be high quality.

Better business decision – Higher quality data can be processed and analyzed more quickly and efficiently leads to more timely, efficient, and high-quality business decisions.

Superior scalability – Cloud data preparation can grow at the pace of the business.

Future proof – Cloud data preparation automatically upgrades so that new capabilities or bug fixes can be triggered as soon as they are released. This allows organizations to stay ahead of the future betterment without risking delays or additional costs.

Accelerated data usage and collaboration – Doing data preparation in the cloud is always on, does not require any technical installation, and lets teams collaborate on the work for faster results.

Now, The Self-service Data Preparation process has become faster and more accessible to a wider variety of users.

To learn more about data preparation, Schedule a demo.

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Smart Waste Management: AI Leads the Way

The amount of waste generated in the world amounted to billions (metric tons) in quantity at the end of 2020. With the growing pressure of the human population, it is set to escalate further. In the developed nation, waste management is a profit-making market vertical. Innovative methodologies and techniques are helping make waste management seem an opportunity to bank upon. The sector is steadily progressing at a CAGR of 5.5% annually. Meanwhile, the global waste management market is set to reach USD 2.3 trillion in the upcoming five to six years.

Waste management scenario in the world

In terms of waste management, the sector encompasses – waste dumping, recycling, and minimization. The main categories are segmented as municipal waste, industrial waste, e-waste, plastic waste, biomedical waste, and others, across the world. As per the World Bank report, the regions producing waste across the world are East Asia and the Pacific region. Often, changes in incremental income in low-income levels have resulted in the production of more solid waste and other types of waste; meanwhile, waste production has also been associated with factors like high income and population to add to the numbers.

Here is a snapshot of trends in waste production, especially solid waste category in the past few years and what the leading trends look like, region-wise.

Image credit: datatopics.worldbank.org

Several entities in the waste management domain have emerged in the past few years. By making use of comprehensive processes to deal with escalating waste piles, many firms belonging to North America and European regions especially, are developing techniques to process the waste and are diligently working on waste minimization procedures. In this goal, technology has become integral to handling the burden posed by categories of waste. Some of the leading nations to work towards developing innovative ways for waste handling and minimization are – Germany, Switzerland, Austria, Sweden, Singapore, among others.

Solving waste management concerns

The primary methods used for waste management include landfill, incineration, composting, and recycling. Out of these, incineration and composting help in reducing the volume of waste to a considerable extent. Other methods to tackle waste include disposal at compost areas, volume reductions plants, borrow pit reclamation areas, and processing locations. While landfilling or decomposition contributes to GHG or greenhouse emissions that cause maximum negative effects than harmful carbon dioxide or CO2. As an active contributor in producing GHG, waste decomposition is far more harmful than carbon dioxide for the environment. Starting from open waste dumps to waste decomposition, the main reason why waste management is mandatory is the deterioration of the environment and human surroundings. Today, it is being helmed as a leading cause of climate change and creating various health risks. Additionally, waste in various forms is posing a great health risk to health workers who are involved in the collection and dumping of the waste on a day-to-day basis.

Although, things are changing fast now with the adoption of practices that are carried out through technological intervention. Technology-led initiatives in waste minimization are influencing the way waste is collected, transported, and sent for recycling. With the onset of internet-of-things or IoT, the possibilities and methods to recycle, upcycle and decomposition processes have become more streamlined and attainable.

The AI-enabled smart waste management

In terms of waste management, traditional waste management techniques have proven to be complex, labor-intensive, and often pose a risk to the life of sanitation workers and staff. On the other hand, a connected ecosystem inspired by IoT has paved the way for the application of AI and Machine Learning models for channeling multiple elements for better Urban Planning and smart cities or cities of the future.

Several developed nations have successfully implemented the AI-enabled waste management infrastructure to reduce waste and process recyclable material. Smart Bins equipped with scanners can scan each and every object discarded by an individual and save the data for transfer remotely through a sensor. The bins can segregate different types of waste like metal, paper, glass, plastic, organic, etc while it gets detected as a frozen inference graph through a camera attached inside with the processing unit. AI programs powered by Machine learning and accurate computer vision training data help in classifying different types of waste images and help in the detection of their categories. Post this, an embedded ultrasonic sensor device also checks the filling level and notifies the owner of the usage. Once the trash bins are filled, the sensors notify centralized waste management systems, which then turn up to collect the waste.

Further, once the collection of trash is done, the Smart Bins are taken to waste processing facilities. Herein, the waste processing facilities working with Artificial Intelligence-based programs, identify the types of waste material and start the segregation on the basis of inference graph data. The segregated waste is sent for the next level of processing for various other methods of waste recycling. Items like metal, cardboard, plastic, wood, and electronic equipment are recycled and made contamination-free for the production of goods.

Last word

The never-ending cycle of waste production and disposal has crippled the existing infrastructure and over-pressurized manpower for a long time. AI-enabled smart waste management systems are a viable answer to the health risks posed, time and energy costs involved in the collection and disposal of waste. With the burden of the growing population and exhaustion of landfill sites, smart waste management has become an imperative and a must-have option to live on a waste-free planet. Not merely this, it will help in tackling waste disposal issues but will also contribute to the creation of a healthier environment. Rather than following decades-old techniques, Smart Waste Management-focused AI applications have opened up new facets to tackle the persistent problem of waste management, especially in countries with swelling populations.

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Industry 4.0 in CNC Machine Monitoring

The demand for Computer Numerical Control (CNC) equipment is gradually increasing and performing to expect a huge growth over the coming years. For this an annual growth rate of more than six percent. CNC machining plays a major role in present manufacturing and helps us create a diverse range of products in several industries, from agriculture, automotive, and aerospace to Semiconductor and circuit boards.

Nowadays, machining has developed rapidly in periods of processing complexity, precision, machine scale, and automation level. In the development of processing quality and efficiency, CNC machine tools play a vital role.

Faststream Technologies has implemented the IoT-enabled CNC machine monitoring solutions, which creates machine-to-machine interaction resulting in automated operations and less manual intervention.

Embedded the IoT sensors on CNC machines that can measure various parameters and send them to a platform from where the state and operation of the machines can be fully supervised. Furthermore, CNC machines can scrutinize the data collected from sensors to perpetually replace tools, change the degree of freedom, or perform any other action.

ADVANTAGES:

An Enterprise can leverage the following advantages by coalescence of Industry 4.0 and CNC.

Predictive Maintenance:

CNC Machine operators and handlers embrace the Industrial IoT which allows them to appropriately interconnect with their CNC machines in many ways through smartphones or tablets. Therefore the operators can monitor the condition of machines at all times remotely using Faststream’s IoT-based CNC machine monitoring.

This remote and real-time monitoring aids the machine operating person to program a CNC for a checkup or restore.

On the other hand, these can also arrange their CNC machines to send alerts or notifications to operators whenever machines deem themselves due for tuning or maintenance. In another term, the machine will raise red flags about complications such as a rise in temperature, increased vibrations, or tool damage.

Reducing Downtime and Efficient Machine Monitoring :

Digital Transformation in CNC Machine solutions has broad competence and is not restricted to distant control and programmed maintenance for CNC machines.

Reduce machine downtime and escalate overall equipment effectiveness by using our IoT system and grasping its real-time alert features. The Alerts received from machines can be used to do predictive measures and unexpected breakdown of tools or any other element of a CNC machine.

Faststream Technologies similar solutions to its clients by arranging the IoT energy management solution for their CNC machines. Pre-executing these solutions, the client was facing difficulties with the future breakdown of their machines. Faststream’s IoT solution guided them to retain a clear insight into the running hours of their CNC machines, which in turn gave them exact thoughts of how they were maintaining their production run-time.

Machine downtime reducing solutions can be utilized for a chain of CNC machines to not only ameliorate their processing but also to boost the machine synchronization process in industrial inception and realize the operational eminence.

Less manual effort and Worker Safety:

For the bigger enactment, the technology of Industrial IoT can also be implemented to bring down manual efforts, or in other terms, mitigate the possibility of workers’ injury in the factory operation process.

From this action, machine-to-machine synchronization and interrelation come into the picture. The synergy between machines will result in more interpretation between various electromechanical devices, which will lead to automated operations in a Manufacturing unit.

Many companies are already working towards the development of smart robots and machines that can.

Several Companies that perform on smart robots and machine development can work on pre-programmed tasks and retaliation to the existing needs of CNC machines for bringing down the extra strain of quality operation from the manual workforce. All these robots can perform confined and elegant work like opening & close the slab of a CNC machine or reform the tool whenever sharpness is required.

Apart from the lowering injuries in the workshop, our Industry 4.0 in CNC Machine also helps in lowering material wastage and betterment the efficiency of CNC machines, which will help in the rise in production of exact elements in a shorter time frame.

CONCLUSION:

CNC machines are electromechanical devices that can operate tools on a different range of axes with more accuracy to generate a small part as per command put through a computer program. These can run faster than any other non-automated machine as well as can generate further objects with high accuracy from any type of design.

Using the technology of the Industrial Internet of Things(IIOT), the competence of a company can be boosted even further, though CNC machines are themselves proficient in uplifting a machine to a new peak.

Faststream Technologies is a cutting-edge IoT solution provider that assists factories and workshops to integrate their CNC machines with Industry 4.0 solutions.

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S2, A next generation data science toolbox

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