Twenty Projects in Data Science Using Python (Part-I)

Young and dynamic data science and machine learning enthusiasts are all are very interested in making a career transition by learning and doing as much hands-on learning as possible with these technologies and concepts as Data Scientists or Machine Learning Engineers or Data Engineers or Data Analytics Engineers. I believe they must have the Project Experience and a job-winning portfolio in hand before they hit the interview process.

Certainly, this interview process would be challenging, NOT only for the freshers, but also for experienced individuals since these are all new techniques, domain, process approach, and implementation methodologies that are totally different from traditional software development. Of course, we could adopt an agile mode of delivery and no excuse from modern cloud adoption techniques and state beyond all industries and domains, who are all looking and interested in artificial intelligence and machine learning (AI and ML) and its potential benefits.

In this article, let’s discuss how to choose the best data science and ML projects during the capstone stages of your schools, colleges, training institutions, and specific job-hunting perspective. You could map this effort with our journey towards getting your dream job in the data science and machine learning industry.

Without much ado, here are the top 20 machine learning projects that can help you get started in your career as a machine learning engineer or data scientist. Let us move into a curated list of data science and machine learning projects for practice that can be a great add-on to your portfolio –

1. Data Science Project – Ultrasound Nerve Segmentation

Problem Statement & Solution

In this project, you will be working on building a machine learning model that can identify nerve structures in a data set of ultrasound images of the neck. This will help enhance catheter placement and contribute to a more pain-free future.

Even the bravest patients cringe at the mention of a surgical procedure. Surgery inevitably brings discomfort, and oftentimes involves significant post-surgical pain. Currently, patient pain is frequently managed using narcotics that bring a number of unwanted side effects.

This data science project’s sponsor is working to improve the pain management system using indwelling catheters that block or mitigate pain at the source. These pain management catheters reduce dependence on narcotics and speed up patient recovery.

The project objective is to precisely identify the nerve structures in the given ultrasound images, and this is a critical step in effectively inserting a patient’s pain management catheter. This project has been developed in python language, so it is easy to understand the flow of the project and the objectives. They must build a model that can identify nerve structures in a dataset of given ultrasound images of the neck. Doing so would improve catheter placement and contribute to a more pain-free future.

Let see the simple workflow.

Certainly, this project would help us to understand the image classification and highly sensitive area of analysis in the medical domain.

Take away and outcome and of this project experience.

• Understanding what image segmentation is.
• Understanding of subjective segmentation and objective segmentation
• The idea of converting images into matrix format.
• How to calculate euclidean distance.
• Scope of what dendrogram are and what they represent.
• Overview of agglomerative clustering and its significance
• Knowledge of VQmeans clustering
• Experiencing grayscale conversion and reading image files.
• A practical way of converting masked images into suitable colours.
• How to extract the features from the images.
• Recursively splitting a tile of an image into different quadrants.

2. Machine Learning project for Retail Price Optimization

Problem Statement & Solution

In this machine learning pricing project, we must implement retail price optimization and apply a regression trees algorithm. This is one of the best ways to build a dynamic pricing model, so developers can understand how to build models dynamically with commercial data which is available from a nearby source and visualization of the solution is tangible.

In this competitive business world “PRICING A PRODUCT” is a crucial aspect. So, we must gather a lot of thought process into that solution approach. There are different strategies to optimize the pricing of products. And must take extra care during the pricing of the products due to their sensitive impact on the sales and forecast. While there are products whose sales are not very affected by their price changes, they could be luxury items or essentials products in the market. This machine learning retail price optimization project will focus on the former type of products.

This project clearly captures the data and aligns with the “Price Elasticity of Demand” phenomenon. This exposes the degree to which the effective desire for something changes as its price the customers desire could drop sharply even with a little price increase, I mean directly proportional relationship. Generally, economists use the term elasticity to denote this sensitivity to price increases.

In this Machine Learning Pricing Optimization project, we will take the data from the café shop and, based on their past sales, identify the optimal prices for their list of items, based on the price elasticity model of the items. For each café item, the “Price Elasticity” will be calculated from the available data and then the optimal price will be calculated. A similar kind of work can be extended to price any products in the market. 

Take away and Outcome and of this project experience.

• Understanding the retail price optimization problem
• Understanding of price elasticity (Price Elasticity of Demand)
• Understanding the data and feature correlations with the help of visualizations
• Understanding real-time business context with EDA (Exploratory Data Analysis) process
• How to segregate data based on analysis.
• Coding techniques to identify price elasticity of items on the shelf and price optimization.

3. Demand prediction of driver availability using multistep Time Series Analysis

Problem Statement & Situation

In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi-step time series analysis. This project is an interesting one since it is based on a real-time scenario.

We all love to order food online and do not like to experience delivery fee price variation. Delivery charges are always highly dependent on the availability of drivers in your area in and around, so the demand of orders in your area, and distance covered would greatly impact the delivery charges. Due to driver unavailability, there is an impact in delivery pricing increasing and directly this will hit the many customers who have dropped off from ordering or moving into another food delivery provider, so at the end of the day food suppliers (Small/medium scale restaurants) are reducing their online orders.

 To handle this situation, we must track the number of hours a particular delivery driver is active online and where he is working and delivering foods, and how many orders are in that area, so based on all these factors certainly, we can efficiently allocate a defined number of drivers to a particular area depending on demand as mentioned earlier.

Take away and Outcome and of this project experience.

• How to convert a Time Series problem to a Supervised Learning problem.
• What exactly is Multi-Step Time Series Forecast analysis?
• How does Data Pre-processing function in Time Series analysis?
• How to do Exploratory Data Analysis (EDA) on Time-Series?
• How to do Feature Engineering in Time Series by breaking Time Features to days of the week, weekends.
• Understand the concept of Lead-Lag and Rolling Mean.
• Clarity of Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) in Time Series.
• Different strategic approaches to solving Multi-Step Time Series problem
• Solving Time-Series with a Regressor Model
• How to implement Online Hours Prediction with Ensemble Models (Random Forest and Xgboost)

4. Customer Market Basket Analysis using Apriori and FP- growth algorithms

Problem Statement & Solution

In this project, anyone can learn how to perform Market Basket Analysis (MBA) with the application of Apriori and FP growth algorithms based on the concept of association rule learning, one of my favourite topics in data science. 

Mix and Match is a familiar term in the US, I remember I used to get the toys for my kid. It was the ultimate experience you know. Same time keeping things together nearby, like bread and jam–shaving razor and cream, these are the simple examples for MBA, and this is making the customer buy additional purchases more likely.

It is a widely used technique to identify the best possible mix of products or services that comes together commonly. This is also called “Product Association Analysis” or “Association Rules”. This approach is best fit physical retail stores and even online too. In other ways, it can help in floor planning and placement of products.

5. E-commerce product reviews – Pairwise ranking and sentiment analysis.

Problem Statement & Solution

Product recommendation systems for the products which are sold over the online-based pairwise ranking and sentiment analysis. So, we are going to perform sentiment analysis on product reviews given by the customers who are all purchased the items and ranking them based on weightage. Here, the reviews play a vital role in product recommendation systems.

Obviously, reviews from customers are very useful and impactful for customers who are going to buy the products. Generally, a huge number of reviews in the bucket would create unnecessary confusion in the selection and buying interest on a specific product. If we have appropriate filters from the collective informative reviews. This proportional issue has been attempted and addressed in this project solution.

This recommendation work has been done in four phases.

• Data pre-processing/filtering
  • Which includes.
    • Language Detection
    • Gibberish Detection
    • Profanity Detection
  • Feature extraction,
  • Pairwise Review Ranking, 

The outcome of the model will be a collection of the reviews for a particular product and its ranking based on relevance using a pairwise ranking approach method/model.

Take away and Outcome and of this project experience.

• EDA Process
  • Over Textual Data
  • Extracted Featured with Target Class
• Using Featuring Engineering and extracting relevance from data
• Reviews Text Data Pre-processing in terms of
  • Language Detection
  • Gibberish Detection
  • Profanity Detection, and Spelling Correction
• Understand how to find gibberish by Markov Chain Concept
• Hands-On experience on Sentiment Analysis
  • Finding Polarity and Subjectivity from Reviews
• Learning How to Rank – Like Pairwise Ranking
• How to convert Ranking into Classification Problem
• Pairwise Ranking reviews with Random Forest Classifier
• Understand the Evaluation Metrics concepts
  • Classification Accuracy and Ranking Accuracy

6. Customer Churn Prediction Analysis using Ensemble Techniques

Problem Statement & Solution

In some situations, the customers are closing their accounts or switching to other competitor banks for too many reasons. This could cause a huge dip in their quarterly revenues and might significantly affect annual revenues for the enduring financial year, this would directly cause the stocks to plunge and the market cap to reduce considerably. Here, the idea is to be able to predict which customers are going to churn, and how to retain them, with necessary actions/steps/interventions by the bank proactively.

In this project, we must implement a churn prediction model using ensemble techniques.

Here we are collecting customer data about his/her past transactions details with the bank and statistical characteristics information for deep analysis of the customers. With help of these data points, we could establish relations and associations between data features and customer’s tendency to possible churn. Based on that, we will build a classification model to predict whether the specific set of customers(s) will indeed leave the bank or not. Clearly draw the insight and identify which factor(s) are accountable for the churn of the customers.

Take away and Outcome and of this project experience.

• Defining and deriving the relevant metrics
• Exploratory Data Analysis
  • Univariate, Bivariate analysis,
  • Outlier treatment
  • Label Encoder/One Hot Encoder
• How to avoid data leakage during the data processing
• Understanding Feature transforms, engineering, and selection
• Hands-on Tree visualizations and SHAP and Class imbalance techniques
• Knowledge in Hyperparameter tuning
  • Random Search
  • Grid Search
• Assembling multiple models and error analysis.

7. Build a Music Recommendation Algorithm using KKBox’s Dataset.

Problem Statement & Solution 

Music Recommendation Project using Machine Learning to predict the best chances of a user listening and loving a song again after their very first noticeable listening event. As we know, the most popular evergreen entertainment is music, no doubt about that. There might be a mode of listening on different platforms, but ultimately everyone will be listening to music with this well-developed digital world era.  Nowadays, the accessibility of music services has been increasing exponentially ranging from classical, jazz, pop etc.,

Due to the increasing number of songs of all genres, it has become very difficult to recommend appropriate songs to music lovers. The question is that the music recommendation system should understand the music lover’s favourites and inclinations to other similar music lovers and offer the songs to them on the go, by reading their pulse.

In the digital market, we have excellent music streaming applications available like YouTube, Amazon Music, Spotify etc., All they have their own features to recommend music to music lovers based on their listening history and first and best choice. This plays a vital role in this business to catch the customers on the go. Those recommendations are used to predict and indicate an appropriate list of songs based on the characteristics of the music, which has been heard by music lovers over the period.

This project uses the KKBOX dataset and demonstrates the machine learning techniques that can be applied to recommend songs to music lovers based on their listening patterns which were created from their history.

Take away and Outcome and of this project experience.

• Understanding inferences about data and data visualization
• Gaining knowledge on Feature Engineering and Outlier treatment
• The reason behind Train and Test split for model validation
• Best Understanding and Building capabilities on the algorithm below
  • Logistic Regression model
  • Decision Tree classifier
  • Random Forest Classifier
  • XGBoost model

8.Image Segmentation using Masked R-CNN with TensorFlow

Problem Statement & Solution

Fire is one of the deadliest risk situations. Generally, fire can destroy an area completely in a very short span of time. Another end this leads to an increase in air pollution and directly affects the environment and an increase in global warming. This leads to the loss of expensive property. Hence early fire detection is very important.

The Object of this project is to build a deep neural network model that will give precise accuracy in the detection of fire in the given set of images. In this Deep Learning-based project on Image Segmentation using Python language, we are going to implement the Mask R-CNN model for early fire detection.

In this project, we are going to build early fire detection using the image segmentation technique with the help of the MRCNN model. Here, fire detection by adopting the RGB model (Color: Red, Green, Blue), which is based on chromatic and disorder measurement for extracting fire pixels and smoke pixels from the image. With the help of this model, we can locate the position where the fire is present, and which will help the fire authorities to take appropriate actions to prevent any kind of loss.

Take away and Outcome and of this project experience.

• Understanding the concepts
  • Image detection
  • Image localization
  • Image segmentation
  • Backbone
    • Role of the backbone (restnet101) in Mask RCNN model
  • MS COCO
• Understanding the concepts
  • Region Proposal Network (RPN)
  • ROI Classifier and bounding box Regressor.
• Distinguishing between Transfer Learning and Machine Learning.
• Demonstrating image annotation using VGG Annotator.
• The best understanding of how to create and store the log files per epoch.

9. Loan Eligibility Prediction using Gradient Boosting Classifier

Problem Statement & Solution

In this project, we are predicting if a loan should be given to an applicant or not for the given data of various customers who are all seeking the loan based on several factors like their credit score and history. The ultimate aim is to avoid manual efforts and give approval with the help of a machine learning model, after analyzing the data and processing for machine learning operations. On the top of the machine, the learning solution will look at different factors based on testing the dataset and decide whether to grant a loan or not to the respective individual.

In this ML problem, we use to cleanse the data and fill in the missing values and bringing various factors of the applicant like credit score, history and from those we will try to predict the loan granting by building a classification model and the output will be giving output in the form of probability score along with Loan Granted or Refused as output from the model.

Take away and Outcome and of this project experience.

• Understanding in-depth:
  • Data preparation
  • Data Cleansing and Preparation
  • Exploratory Data Analysis
  • Feature engineering
  • Cross-Validation
  • ROC Curve, MCC scorer etc
  • Data Balancing using SMOTE.
  • Scheduling ML jobs for automation
• How to create custom functions for machine learning models
• Defining an approach to solve
  • ML Classification problems
  • Gradient Boosting, XGBoost etc

10.Human Activity Recognition Using Multiclass Classification

Problem Statement & Solution

In this project we are going to classify human activity, we use multiclass classification machine learning techniques and analyze the fitness dataset from a smartphone tracker. 30 activities of daily participants have been recorded through a smartphone with embedded inertial sensors and build a strong dataset for activity recognition point of view. Target activities are WALKING, WALKING UPSTAIRS, WALKING DOWNSTAIRS, SITTING, STANDING, LAYING, by capturing 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The objective is to classify activities mentioned above among 6 and 2 different axials. This was captured by an embedded accelerometer and gyroscope in the smartphone. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets as 70% for training and 30% for test data.

Take away and Outcome and of this project experience.

• Understanding
  • Data Science Life Cycle
  • EDA
  • Univariate and Bivariate analysis
  • Data visualizations using various charts.
  • Cleaning and preparing the data for modelling.
  • Standard Scaling and normalizing the dataset.
  • Selecting the best model and making predictions
• How to perform PCA to reduce the number of features
• Understanding how to apply
  • Logistic Regression & SVM
  • Random Forest Regressor, XGBoost and KNN
  • Deep Neural Networks
• Deep knowledge in Hyper Parameter tuning for ANN and SVM.
• How to plot the confusion matrix for visualizing the result
• Develop the Flask API for the selected model.

Project Idea Credits – ProjectPro helps professionals get their work done faster and with practical experience with verified reusable solution code, real-world project problem statements, and solutions from various industry experts.

So far, We have discussed 10 different projects, Hope you could feel each one of them at least high level and clear goal of what is the objective of the project and learning take away While doing the projects as hands-on.

I am sure you could feel the essence of those and digesting each concept in Data Science and Machine Learning. Learn More always! 

Will discuss 10 more projects in a short while, Until then, Bye! See you!

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KDNuggets.com Launches Free Outlier Analysis Boxplot Template

Canadians in the civil service, academia or legal profession who use WordPerfect (Corel) and its Quattro Pro spreadsheet application should also be able to download and test the free template since it is developed on a VBA platform. You can let me know. And, anyone – anywhere – who uses a Windows-based spreadsheet application other than Excel — that is likely developed on a VBA platform — should also be able to run the free template (and others) in my store. You can let me know if this is true.

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An Introduction to Statistical Sampling

Image Source: Statistical Aid

Sampling is a statistical procedure of selecting some representative part from an existing population or study area. Specifically, draw a sample from the study population using some statistical methods. For example-
if we want to calculate the average age of Bangladeshi people then we can not deal with the whole population. In that time we must have to deal with some representative part of this population. This representative part is called sample and the procedure is called sampling.

Why need sampling

—  It makes possible the study of a large population which contains different characteristics.

—  It is for economy.

—  It is for speed.

—  It is for accuracy.

—  It saves the sources of data from being all consumed.

Sometimes we can’t work with population such as blood test, in that situation sampling is must.

Types 

Probability Sampling

It is based on the concept of random selection where each population elements have a non-zero chance to occur as a sample. Sampling techniques can be divided into two categories: probability and non-probability. Randomization or chance is the core of probability sampling techniques.

For example, if a researcher is dealing with a population of 100 people, each person in the population would have the odds of 1 out of 100 for being chosen. This differs from non-probability sampling, in which each member of the population would not have the same odds of being selected.

Different types of probability sampling

Applications

·    In opinion poll, a relatively small number of persons are interviewed and their opinions on current issues are solicited in order to discover the attitude of the community as a whole.

·    At border stations, customs officers enforce the laws by checking the effects of only a small number of travelers crossing the border.

·    A departmental store wises to examine whether it is losing or gaining customers by drawing a sample from its lists of credit card holders by selecting every tenth name.

·    In a manufacturing company, a quality control officer take one sample from every lot and if any sample is damage then he reject that lot.

Advantages

—  Creates samples that are highly representative of the population.

—  Sampling bias is tens to zero.

—  Higher level of reliability of research findings.

—  Increased accuracy of sample error estimation.

—  The possibility to make inferences about the population.

Disadvantages

—  Higher complexity compared to non-probability sample.

—  More time consuming, especially when creating larger sample.

—  Usually more expensive.

Non-Probability sampling

The process of selecting a sample from a population without using statistical probability theory is called non-probability sampling.

Example

Lets say that the university has roughly 10000 students. These 10000 students are our population (N). Each of the 10000 students is known as a unit, but its hardly possible to get known and select every student randomly.

Here we can use Non-Random selection of sample to produce a result.

Applications

·   It can be used when demonstrating that a particular trait exist in the population.

·    It can also be useful when the researcher has limited budget, time and workforce.

Advantages

·        Select samples purposively

·        Enable researchers to reach difficult to identify members of the population.

·        Lower cost

·        Limited time.

Disadvantage

Difficult to make valid inference about the entire population because the sample selected is not representative.

We cannot calculate confidence interval.

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Using IDP to jump-start your AI journey

At the end of 2020, Forrester Research analysts predicted that more than a third of companies would look to AI in 2021 to help with workplace disruption caused by the pandemic – that is, the shift to remote and hybrid work. This includes things like intelligent document processing (IDP) and customer service agent augmentation, among other functions.

In other words, now is the time for AI to shine – and for organizations looking to launch new AI projects, the good news is that it’s not an all-at-once proposition. Smaller applications, like those mentioned above, can be the essential first steps. IDP, in particular, can be a great entry point for organizations looking to start the artificial intelligence (AI) journey.

Dipping your toes in the AI water

With some technologies, there’s no real way to take baby steps; you have to go from zero to 100. But with AI and machine learning (ML) adoption, it really is a journey. You can try it out first with small, isolated projects, applying AI to one function at a time to see the results. It’s very much a crawl, walk, run approach. Unlike many transactional systems like ERP or CRM, AI/ML application deployment in the enterprise world is not a sudden, life-changing event. In fact, AI/ML should be adopted in a gradual manner to achieve the greatest success.

When you think about document processing, it may seem low on the priority list – one small problem in the scheme of things. But the reality is that it’s an important but often overlooked component of so many functions across the enterprise. And in that respect, yes, it’s a small piece of the bigger picture, but a key one.

And when it comes to adopting automation and AI, IDP can be a comparatively easy place to start. For a business leader who wants to start applying automation and AI within their enterprise, it represents a relatively low-risk steppingstone.

Understanding IDP

Today’s enterprises generate and receive a mountain of documents, both digital and physical. These are often manually processed by humans, who enter the relevant data into application systems for storage and future retrieval purposes. This approach is time-consuming and error-prone. It relies entirely on human efforts to process documents, which can lead to long cycle times, reduced productivity, unwanted errors and increased costs.

IDP promises to make it easier to automate these workflows through document capture, optical character recognition (OCR) and natural language processing (NLP). The premise behind IDP is to digitize the entire document processing workflow across business processes by eliminating the touchpoints that requires manual intervention. Doing away with this manual intervention not only reduces costs, but it also reduces errors and ultimately helps achieve new levels of productivity.

More specifically, IDP intelligently classifies, captures and extracts all data from documents entering the workflow. It then organizes the information based on business need. Once the data has been validated and verified, the system automatically exports it to downstream business applications. In today’s advanced IDP solutions, the entire process is powered by AI/ML algorithms to make business processes more resilient to disruptions and help mitigate risks.

This is unlike robotic process automation (RPA), which doesn’t really use AI and is mostly rule-based and driven by templates approach. It eliminates repetitive tasks but can’t provide the other benefits that IDP brings to the table.

A gateway to more business and process automation

Documents underpin so many different functions and applications, be it Accounts Payable, CRM, ERM or business process management. Being able to apply understanding and insight to integrated documents can be a huge differentiator for many other enterprise applications.

A key to the success of AI in enterprise applications is whether you believe your AI is trustworthy. Trust can be built by verification and validation. IDP provides the opportunity to easily verify and validate whether AI is doing what it is supposed to be doing. This makes it easy for enterprises to adapt AI for other key business applications once trust has been established.

All of this fits into the bigger picture of meeting the aforementioned goals – cutting costs, reducing time spent on manual tasks, reducing risk of human error and increasing productivity – throughout the enterprise.

The journey begins

To paraphrase Ralph Waldo Emerson, when it comes to AI/ML adoption, it’s a journey, not a destination. AI can be invaluable in helping resolve real business issues and recommend new products or services, for instance, but if they are improperly set up, they can quickly become expensive failures. It makes sense, then, to start with smaller applications of AI and then slowly expand.

IDP can be an important and key first step in that journey – an opportunity to start with automating of document processing before expanding to other functions and implementations across the enterprise. AI-powered intelligent document processing quickly demonstrates business value and instills faith in the power of AI across stakeholder groups. They will then be more willing to expand into other AI initiatives that will benefit your organization in additional ways.

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Six Trends in Mobile Application Development To Watch

It is predicted that there are 7 billion mobile users around the world in 2021. It’s projected that this figure will continue to rise as technology becomes more accessible each passing day. Mobile phones have integrated into our lives seamlessly over the past decade.

Regardless of your industry, the mobile application development industry has been genuinely altering and redefining business for quite a while now. Almost every company needs to amalgamate the latest mobile app development trends and extend its marketing strategy to gain traction towards optimum growth and reach its targeted market effectively.

Kodak, Compaq, Blockbuster Video – what do these companies have in common? Despite being widely popular in the past, they failed to keep up with technology and, therefore, ended up closing or selling out. All this at a time when technology was not changing as fast as it is today. Technology truly is in a constant state of flux. When businesses fail to keep up, they usually tend to go the Blockbuster way. 

The only way to stand out in such an environment is through constant innovation. Whether you are a developer or a business with a mobile app, you must be updated with the latest mobile app trends. Without incorporating these trends into your apps, your mobile might become obsolete.

Apps are being developed faster than ever to meet the rising demand for new content. Consumers today expect services to come with apps with friendly, clean user interfaces. The presentation of your brand through your app can go a long way with the tech-savvy customers of today. Let’s look at the emerging trends in mobile application development that you must keep an eye out for. 

What are the New Trends in Mobile Application Development?

1.Wearables

Wearables have taken the world by storm; whether it’s on the Subway or at the gym, you can see everybody decked out with the latest wearables. Then, the Apple Watch and AirPods paved the way for more development in space. Today, every manufacturer offers its version of the smartwatch and smart earbuds. These are capable of doing everything from helping you navigate to your destination, and some might even let you make a call without your phone being around! With the wearable industry being valued at over $44 billion, it’s safe to say wearables are one of the top mobile application trends.

Wearable Trends in 2021

• Fitness-based tech to stay at the forefront.
• Move to make wearables more independent of the smartphone.

2.On-Demand Development Apps

On-demand development apps were created to fill a void in the mobile app development industry. Building apps required technical expertise and knowledge of coding, but today, the on-demand development model has made building apps so much more accessible. Are you running a business and looking to scale using apps? Chances are, you won’t have to build it yourself at all. There’s a good chance that there’s an on-demand app that will do everything that you expect from it. This statistic says 42% of adults have used on-demand services. The on-demand development model is likely to grow as the demand for simplified app development increases.

On-Demand Apps in 2021

• More industries adopting the on-demand model
• B2B transactions are emphasised

3.Mobile Wallets

The pandemic changed our lifestyles and forced us to adopt a digital-first alternative. Today, everything from buying groceries to paying people for their services is done online. Mobile wallets have simplified online payments and made them accessible to everyone.

As we embrace transferring money online, service providers will push to make their products better and more secure. Security of funds and transactions is one of the primary concerns when it comes to mobile wallet development. Social distancing is the new norm post the pandemic, so contactless payment solutions like Apple Pay and Google Pay solve the problem. Going forward, security and ease of payment will drive innovation in this sector to emerge as a critical mobile application trend.

Mobile Wallet Trends in 2021

• 2 billion users worldwide and counting
• Secure and convenient wallets

4.Cloud-based Apps

Cloud technology has grown so much over the past few years. Cloud storage is growing to become inexpensive as service providers invest in more efficient cloud infrastructure. Cloud technology is the backbone of mobile app development in 2021.

Many things we do on apps today leverage cloud technology, like booking a cab or ordering food. Cloud has made web hosting inexpensive, more load efficient and accessible. This has prompted the quick adoption of the mobile technology trend.

Cloud Trends in 2021

• Efficient cloud infrastructure
• Hybrid cloud solutions
• Quantum computing

5.Smart Things / IoT

The Internet of Things is an ecosystem of intelligent devices that can communicate with other devices over the Internet. Everything from the lights in our homes to the ovens in our kitchens can be controlled through Alexa, Siri, or Google Assistant. This is the future envisioned by the IoT, and we’ve warmed up to it well so far. Manufacturers like Samsung, Xiaomi, Nest, and Honeywell build a solid platform at accessible price points. Some of the intelligent critical mobile application technology trends are:

IoT trends in 2021

• More affordable IoT tech
• Self-driving cars
• Smart home and appliances

6.Augmented Reality (AR) and Virtual Reality (VR)

Who isn’t familiar with Pokemon Go? The game took the world by storm and brought augmented reality into the mainstream. While augmented reality superimposes artificial objects on real-world objects, virtual reality offers an entirely artificial environment.

But games aren’t the only area of application of AR and VR. These technologies can be used to improve the efficacy of training and educational apps. They can give the student a true sense of performing the job at hand. 

Interior designing and marketing are other areas where AR and VR apps are creating game-changing experiences. The app can let the user see how the product will look in a particular space or give you a better idea about its size and shape.  

AR/VR Trends in 2021

• AR/VR in marketing, healthcare, education, and other industries
• Mobile AR technology is going to be at the forefront

Future of Mobile Application Development: 2021 and Beyond

Technology is constantly evolving, with new iterations of technology hitting the shelves every year. These mobile technology trends in the market provide plenty of new opportunities for app developers.

Keeping up with trends is vital to stay on top of the mobile app development space. In 2021, technologies like wearables, the Internet of Things, and cloud computing will continue to catch steam. 

This article was originally published here

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Dominant Data Science Developments in 2021

There’s nothing constant in our lives but change. Over the years, we’ve seen how businesses have become more modern, adopting the latest technology to boost productivity and increase the return on investment. 

Data analytics, big data, artificial intelligence, and data science are the trending keywords in the current scenario. Enterprises want to adopt data-driven models to streamline their business processes and make better decisions based on data analytical insights. 

With the pandemic disrupting industries around the world, SMEs and large enterprises had no option but to adapt to the changes in less time. This led to increasing investments in data analytics and data science. Data has become the center point for almost every organization. 

As businesses rely on data analytics to avoid and overcome several challenges, we see new trends emerging in the industries. AI trends 2021 by Gartner are an example of development. The trends have been divided into three major heads- accelerating change, operationalizing business value, and distribution of everything (data and insights). 

In this blog, we’ll look at the dominating data science developments in 2021 and understand how big data and data analytics are becoming an inherent part of every enterprise, irrespective of the industry. 

1. Big Data on the Cloud 

Data is already being generated in abundance. The problem lies with collecting, tagging, cleaning, structuring, formatting, and analyzing this huge volume of data in one place. How to collect data? Where to store and process it? How should we share the insights with others? 

Data science models and artificial intelligence come to the rescue. However, storage of data is still a concern. It has been found that around 45% of enterprises have moved their big data to cloud platforms. Businesses are increasingly turning towards cloud services for data storage, processing, and distribution. One of the major data management trends in 2021 is the use of public and private cloud services for big data and data analytics. 

2. Emphasis on Actionable Data 

What use is data in its raw, unstructured, and complex format if you don’t know what to do with it? The emphasis is on actionable data that brings together big data and business processes to help you make the right decisions. 

Investing in expensive data software will not give any results unless the data is analyzed to derive actionable insights. It is these insights that help you in understanding the current position of your business, the trends in the market, the challenges and opportunities, etc. Actionable data empowers you to become a better decision-maker and do what’s right for the business. From arranging activities/ jobs in the enterprise, streamlining the workflows, and distributing projects between teams, insights from actionable data help you increase the overall efficiency of the business. 

3. Data as a Service- Data Exchange in Marketplaces 

Data is now being offered as a service as well. How is that possible? 

You must have seen websites embedding Covid-19 data to show the number of cases in a region or the number of deaths, etc. This data is provided by other companies who offer data as a service. This data can be used by enterprises as a part of their business processes. 

Since it might lead to data privacy issues and complications, companies are coming with procedures that minimize the data risk of a data breach or attract a lawsuit. Data can be moved from the vendor’s platform to the buyer’s platforms with little or no disturbance and data breach of any kind. Data exchange in marketplaces for analytics and insights is one of the prominent data analytics trends in 2021. It is referred to as DaaS in short. 

4. Use of Augmented Analytics 

What is augmented analytics? AA is a concept of data analytics that uses AI, machine learning, and natural language processing to automate the analysis of massive data. What is normally handled by a data scientist is now being automated in deliver insights in real-time.    

It takes less time for enterprises to process the data and derives insights from it. The result is also more accurate, thus leading to better decisions. From assisting with data preparation to data processing, analytics, and visualization, AI, ML, and NLP help experts explore data and generate in-depth reports and predictions. Data from within the enterprise and outside the enterprise can be combined through augmented analytics. 

5. Cloud Automation and Hybrid Cloud Services

The automation of cloud computing services for public and private clouds is achieved using artificial intelligence and machine learning. AIOps is artificial intelligence for IT operations. This is bringing a change in the way enterprises look at big data and cloud services by offering more data security, scalability, centralized database and governance system, and ownership of data at low cost. 

One of the big data predictions for 2021 is the increase in the use of hybrid cloud services. A hybrid cloud is an amalgamation of a public cloud and a private cloud platform.

Public clouds are cost-effective but do not provide high data security. A private cloud is more secure but expensive and not a feasible option for all SMEs. The feasible solution is a combination of both where cost and security are balanced to offer more agility. A hybrid cloud helps optimize the resources and performance of the enterprise. 

6. Focus on Edge Intelligence 

Gartner and Forrester have predicted that edge computing will become a mainstream process in 2021. Edge computing or edge intelligence is where data analysis and data aggregation are done close to the network. Industries wish to take advantage of the internet of things (IoT) and data transformation services to incorporate edge computing into the business systems. 

This results in greater flexibility, scalability, and reliability, leading to a better performance of the enterprise. It also reduces latency and increases the processing speed. When combined with cloud computing services, edge intelligence allows employees to work remotely while improving the quality and speed of productivity. 

7. Hyperautomation 

Another dominant trend in data science in 2021 is hyper-automation, which began in 2020. Brian Burke, Research Vice President of Gartner, has once said that hyper-automation is inevitable and irreversible, and anything and everything that can be automated should be automated to improve efficiency. 

By combining automation with artificial intelligence, machine learning, and smart business processes, you can unlock a higher level of digital transformation in your enterprise. Advanced analytics, business process management, and robotic process automation are considered the core concepts of hyper-automation. The trend is all set to grow in the next few years, with more emphasis on robotic process automation (RPA). 

8. Use of Big Data in the Internet of Things (IoT)

Internet of Things (IoT) is a network of physical things embedded with software, sensors, and the latest technology. This allows different devices across the network to connect with each other and exchange information over the internet. By integrating the Internet of Things with machine learning and data analytics, you can increase the flexibility of the system and improves the accuracy of the responses provided by the machine learning algorithm. 

While many large-scale enterprises are already using IoT in their business, SMEs are starting to follow the trend and become better equipped to handle data. When this occurs in full swing, it is bound to disrupt the traditional business systems and result in tremendous changes in how business systems and processes are developed and used. 

9. Automation of Data Cleaning 

For advanced analytics in 2021, having data is not sufficient. We already mentioned in the previous points how big data is of no use if it’s not clean enough for analytics. It also refers to incorrect data, data redundancy, and duplicate data with no structure or format. 

This causes the data retrieval process to slow down. That directly leads to the loss of time and money for enterprises. On a large scale, this loss could be counted in millions. Many researchers and enterprises are looking for ways to automate data cleaning or scrubbing to speed up data analytics and gain accurate insights from big data. Artificial intelligence and machine learning will play a major role in data cleaning automation.

10. Increase in Use of Natural Language Processing 

Famously known as NLP, it started as a subset of artificial intelligence. It is now considered a part of the business processes used to study data to find patterns and trends. It is said that NLP will be used for the immediate retrieval of information from data repositories in 2021. Natural Language Processing will have access to quality information that will result in quality insights. 

Not just that, NLP also provides access to sentiment analysis. This way, you will have a clear picture of what your customers think and feel about your business and your competitors. When you know what your customers and target audience expect, it becomes easier to provide them with the required products/ services and enhance customer satisfaction. 

11. Quantum Computing for Faster Analysis 

One of the trending research topics in data science is Quantum computing. Google is already working on this, where decisions are not taken by the binary digits 0 and 1. The decisions are made using quantum bits of a processor called Sycamore. This processor is said to solve a problem in just 200 seconds. 

However, Quantum computing is very much in its early stages and needs a lot of fine-tuning before it can be adopted by a range of enterprises in different industries. Nevertheless, it has started to make its presence felt and will soon become an integral part of business processes. The aim of using Quantum computing is to integrate data by comparing data sets for faster analysis. It also helps in understanding the relationship between two or more models. 

12. Democratizing AI and Data Science 

We have already seen how DaaS is becoming famous. The same is now being applied to machine learning models as well. Thanks to the increase in demand for cloud services, AI and ML models are easier to be offered as a part of cloud computing services and tools. 

You can contact a data science company in India to use MLaaS (Machine Learning as a Service) for data visualization, NLP, and deep learning. MLaaS would be a perfect tool for predictive analytics. When you invest in DaaS and MLaaS, you don’t need to build an exclusive data science team in your enterprise. The services are provided by offshore companies. 

13. Automation of Machine Learning (AutoML)

Automated machine learning can automate various data science processes such as cleaning data, training models, predicting results and insights, interpreting the results, and much more. These tasks are usually performed by data science teams. We’ve mentioned how data cleaning will be automated for faster analytics. The other manual processes will also follow suit when enterprises adopt AutoML in their business. This is yet in the early stages of development.

14. Computer Vision for High Dimensional Data Analytics 

Forrester has predicted that more than 1/3rd of the enterprises will depend on artificial intelligence to reduce workplace disruptions. The advent of the covid-19 pandemic has forced organizations to make some drastic changes to the business processes. The remote working facility has become necessary for most businesses. Similarly, automation is being considered a better option than relying on workers and the human touch. 

Using computer vision for high-dimensional data analytics is one of the data science trends in 2021 that helps enterprises detect inconsistencies, perform quality checks, assure safe practices, speed up the processes, and perform more such actions. Especially seen in the manufacturing industry, CV is making it possible to automate production monitoring and quality assurance. 

Conclusion 

Data science will continue to be in the limelight in the coming years. We will see more such developments and innovations. The demand for data scientists, data analysts, and AI engineers is set to increase. The easiest way to adopt the latest changes in the business is by hiring a data analytics company. 

Stay relevant in this competitive market by adopting the data-driven model in your enterprise. Be prepared to tackle the changing trends and make the right decisions to increase returns.

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How to Connect Android with Firebase Database

Databases are an integral part of all of our projects. We store, retrieve, erase, and update our data in the database developed by the application or software. Things get more difficult because you try to download and save all of the data in real-time, which means that if you update the data, the updated data can be reflected in the application at that same moment. But don’t worry, Firebase Real-time Database streamlines things. In this article, we will learn how to connect Firebase Real-time Database with our Android application

What is Firebase Real-time Database?

Firebase Real-time Database is a cloud-hosted database that works on Android, IOS, and the web. All data is stored in JSON format, and any modifications to the data are automatically reflected by executing sync through all platforms and devices. This enables us to create more flexible real-time applications with limited effort.

Advantages of Using Firebase Real-time Database

1) Real-time

The data stored in the Firebase Real-time Database will be reflected in real-time, which means if the values in the database change, the change will be reflected back to all users at that point only, with no delay.

2) High Accessibility

 You can access Firebase Real-time Database from different devices such as Android, IOS, and the Web. So for other platforms, you don’t have to write the same code several times.

3) Offline Mode

The most significant advantage of real-time is that if you are not connected to the internet and make some changes in your application, then those changes will be reflected in your application at the time but only on the firebase database. Changes will be updated once you are online. As a result, though there is no internet, the customer feels as if he or she is using the facilities as if there is the internet.

4) Control Access to data

By default, no one is authorized to change the data in the Firebase Real-time Database, but you can control data access, i.e., you can specify which users are entitled to access the data. 

How to Connect Android with Firebase Database

You can connect your Android app firebase using two methods. 

Method1: Use the Firebase Console Setup Workflow

Method2: Use Android Studio Firebase Assistant

Step 1: Create a Database

Open the Firebase console and go to the Real-time Database portion. You’ll be asked to choose a Firebase project that already exists. Follow the steps for creating a database.

1) Test Mode: While this is an excellent way to get started with the mobile and web client libraries, it allows anyone to read and delete your data. After checking, go through the Understand Firebase Real-time Database Rules section. To get started with the web, IOS, or Android SDK, select test mode.

2) Locked Mode: Denies both reads and writes from mobile and web clients. Your authenticated application servers can still access your account.

• Select a location for the database. The database namespace will be of the form databaseName>.region>.firebaseio.com or databaseName>.region>.firebase database.app, depending on the region selection.  Select locations for your project for more details.
• Click Done

Step 2: Register Your App with Firebase

You must register your Android app with your Firebase project to use Firebase with it. Registering your app is often called “adding” your app to your project.

1) Go to the Firebase Console

2) To start the setup workflow, press the Android icon (plat android) or Add an app in the center of the project overview page.

3) Enter your app’s package name in the Android package name field.

4) Enter some additional app information: App nickname and SHA-1 signature certificate for debugging.

5) Click Register App

Step 3: Add a Firebase Configuration File

1) Add the Firebase Android configuration file to your app:

1. Click on Download google-services. Json to obtain your Firebase Android config file (google-services. Json).
2. Move your config file into the module directory of your app.

2) Add the google-services plugin to your Gradle files to support Firebase products in your application.

1. Include the Google Services Gradle plugin in your root-level (project-level) Gradle file (build. gradle). Check if you already have Google’s Maven repository installed.
2. In your module (app-level) Gradle file (usually app/build.gradle), apply the Google Services Gradle plugin

Step 4: Add Firebase SDKs to Your App

1) Declare the dependencies for the Firebase products you want to use with your app using the Firebase Android BoM. Declare them in your module (app-level) Gradle file (typically app/build.gradle).

2) Sync your app to ensure that all dependencies have the necessary versions.

Method2: Use Android Studio Firebase Assistant

Step 1: Update the Android Studio.

Step 2: Create a new project in the Firebase by clicking on the add project.

Step 3: Now open Android Studio and navigate to the Tools menu in the upper left corner.

Step 4: Now click on the Firebase option in the drop-down menu.

Step 5: On the right side of the screen, a menu will appear. It will show the different services provided by Firebase. Select the appropriate service.

Step 6: Now, In the menu of the desired service, choose Connect to Firebase.

Step 7: Add your service’s requirements by selecting the Add [YOUR NAME] to the app option.

The Final Verdict

So, In this article, we have seen two ways of connecting Firebase to an Android app. We hope you enjoyed reading this article. If you would like help programming your app, please Latitude Technolabs Contact us.  If you have any queries or suggestions about this blog, then feel free to ask them in the comment section.

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Exploring BERT Language Framework for NLP Tasks

As artificial intelligence apes the human speech, vision, and mind patterns, the domain of NLP is buzzing with some key developments in place.

NLP is one of the most crucial components for structuring a language-focused AI program, for example, the chatbots which readily assist visitors on the websites and AI-based voice assistants or VAs. NLP as the subset of AI enables machines to understand the language text and interpret the intent behind it by various means. A hoard of other tasks is being added via NLP like sentiment analysis, text classification, text extraction, text summarization, speech recognition, and auto-correction, etc.

However, NLP is being explored for many more tasks. There have been many advancements lately in the field of NLP and also NLU (natural language understanding) which are being applied on many analytics and modern BI platforms. Advanced applications are using ML algorithms with NLP to perform complex tasks by analyzing and interpreting a variety of content.

About NLP and NLP tasks

Apart from leveraging the data produced on social media in the form of text, image, video, and user profiles, NLP is working as a key enabler with the AI programs. It is heightening the application of Artificial Intelligence programs for innovative usages like speech recognition, chatbots, machine translation, and OCR or optical character recognition. Often the capabilities of NLP are turning the unstructured content into useful insights to predict the trends and empower the next level of customer-focused product solutions or platforms.

Among many, NLP is being utilized for programs that require to apply techniques like:

Machine translation: Using different methods for processing like statistical, or rule-based, with this technique, one natural language is converted into another without impacting its fluency and meaning to produce text as result.

Parts of speech tagging: NLP technique of NER or named entity recognition is key to establish the relation between words. But before that, the NLP model needs to tag parts of speech or POS for evaluating the context. There are multiple methods of POS tagging like probabilistic or lexical.

Parts of speech tagging: NLP technique of NER or named entity recognition is key to establish the relation between words. But before that, the NLP model needs to tag parts of speech or POS for evaluating the context. There are multiple methods of POS tagging like probabilistic or lexical.

Information grouping: An NLP model which requires to classify documents on the basis of language, subject, type of document, time or author would require labeled data for text classification.

Named entity recognition: NER is primarily used for identifying and categorizing text on the basis of name, time, location, company, and more for content classification in programs for academic research, lab reports analysis, or for customer support practices. This often involves text summarization, classification, and extraction.

Virtual assistance: Specifically for chatbots and virtual assistants, NLG or natural language generation is a crucial technique that enables the program to respond to queries using appropriate words and sentence structures.

All about the BERT framework

An open-source machine learning framework, BERT, or bidirectional encoder representation from a transformer is used for training the baseline model of NLP for streamlining the NLP tasks further. This framework is used for language modeling tasks and is pre-trained on unlabelled data. BERT is particularly useful for neural network-based NLP models, which make use of left and right layers to form relations to move to the next step.

BERT is based on Transformer, a path-breaking model developed and adopted in 2017 to identify important words to predict the next word in a sentence of a language. Toppling the earlier NLP frameworks which were limited to smaller data sets, the Transformer could establish larger contexts and handle issues related to the ambiguity of the text. Following this, the BERT framework performs exceptionally on deep learning-based NLP tasks. BERT enables the NLP model to understand the semantic meaning of a sentence – The market valuation of XX firm stands at XX%, by reading bi-directionally (right to left and left to right) and helps in predicting the next sentence.

In tasks like sentence pair, single sentence classification, single sentence tagging, and question answering, the BERT framework is highly usable and works with impressive accuracy. BERT involves two-stage applications – unsupervised pre-training and supervised fine-tuning. It is pre-trained on MLM (masked language model) and NSP (next sentence prediction). While the MLM task helps the framework to learn using the context in right and left layers through unmasking the masked tokens; the NSP task helps in capturing the relation between two sentences. In terms of the technical specifications required for the framework, the pre-trained models are available as Base (12 layers, 786 hidden layers, 12 self attention head, and 110 m parameters) and Large (24 layers, 1024 hidden layer, 16 self attention head, and 340 m parameters).

BERT creates multiple embeddings around a word to find and relate with the context. The input embeddings of BERT include token, segment, and position components.

Since 2018, reportedly the BERT framework is in extensive usage for various NLP models and in deep language learning algorithms. As Bert is open source, there are several variants that have also been in usage, often delivering better results than the base framework such as ALBERT, HUBERT, XLNet, VisualBERT, RoBERTA, MT-DNN, etc.

What makes BERT so useful for NLP?

When Google introduced and open-sourced the BERT framework, it produced highly accurate results in 11 languages simplifying tasks such as sentiment analysis, words with multiple meanings, and sentence classification. Again in 2019, Google utilized the framework for understanding the intent of search queries on its search engine. Following this, it is being widely applied for tasks like answering questions on SquAD (Stanford question answering dataset), GLUE (Generational language understanding evaluation), and NQ datasets, for product recommendation based on product review, deeper sentiment analysis based on user intent.

By the end of 2019, the framework was adopted for almost 70 languages used across different AI programs. BERT helped solve various complexities of NLP models built with a focus on natural languages spoken by humans. Where previous NLP techniques were required to train on repositories of large unlabeled data, BERT is pre-trained and works bi-directionally to establish contexts and predict. This mechanism increases the capability of NLP models further which are able to execute data without requiring it to be sequenced and organized in order. In addition to this, the BERT framework performs exceptionally for NLP tasks surrounding sequence-to-sequence language development and natural language understanding (NLU) tasks.

Endnote:

BERT has helped in saving a lot of time, cost, energy, and infrastructural resources by emerging as the sole enabler in place of building a distinguished language processing model from scratch. By being open-source, it has proved to be far more efficient and scalable than previous language models Word2Vec and Glove. BERT has outperformed human accuracy levels by 2% and has scored 80% on GLUE score and almost 93.2% accuracy on SquAD 1.1. BERT can be fine-tuned as per user specification while it is adaptable for any volume of content.

The framework has been a valuable addition to NLP tasks by introducing pre-trained language models while proving as a reliable source to execute NLU and NLG tasks through the availability of its multiple variants. The BERT framework definitely provides the opportunity to watch out for some exciting New Development in NLP in the near future.

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Analytics Maturity: from Descriptive to Autonomous Analytics

In Chapter 8 of my new book “The Economics of Data, Analytics, and Digitalization Transformation”, I discuss the 8 Laws of Digital Transformation.  My goal for chapter 8 was to push folks out of their comfort zones, especially with respect to how they are defining Digital Transformation success. Why? Because too many folks don’t really understand “Digital Transformation.”  For example, from the Forbes article “100 Stats On Digital Transformation And Customer Experience”, we get the following factoid:

“21% of companies think they’ve already completed digital transformation”

To that factoid, I say BS!  I think those 21% of companies are confusing Digitalization with Digital Transformation. Digitalization is the conversion of human-centric analog tasks into digital capabilities. For example, digitalization is replacing human meter readers, who manually record home electricity consumption data monthly, with internet-enabled meter readers that send a continuous stream of electricity consumption data to the utility company.

But Digital Transformation is something bigger, harder, and much more valuable:

Digital Transformation is where organizations have created a continuously learning and adapting culture, both AI‐driven and human‐empowered, that seeks to optimize AI-Human interactions to identify, codify, and operationalize actionable customer, product, and operational insights to optimize operational efficiency, reinvent value creation processes, mitigate new operational and compliance risk, and continuously create new revenue opportunities.

Digital Transformation is about predicting what’s likely to happen, prescribing recommended actions and continuously-learning and adapting (autonomous) faster than your competition.

Digital Transformation is about creating an organization that continuously explores, learns, adapts, and re-learns. Wash, rinse, repeat. Every customer engagement is an opportunity to learn more about the preferences and behaviors of that customer. Every product interaction or usage is an opportunity to learn more about the performance and behaviors of that product. Every employee, supplier and partner engagement provide an opportunity to learn more about the effectiveness and efficiencies of your business operations.

To create a continuously learning intelligent organization, organizations need to master the transition from reporting to predicting to prescribing to autonomous analytics. Now I know that most analytics maturity models stop at prescriptive analytics (descriptive to predictive to prescriptive), but that’s old school thinking.  The world is changing, and the new analytics maturity goal is autonomous analytics (Figure 1).

Figure 1: Analytics Maturity Curve: From Descriptive to Autonomous Analytics

This Analytics Maturation Curve provides a guide to help organizations transition through the three levels of analytics maturity—from reporting to autonomous:

• Level 1: Insights and Foresight. This is the foundational level of advanced analytics. Level 1 includes statistical analysis, data mining, and descriptive and exploratory analytics (e.g., clustering, classification, regression) to quantify cause-and-effect, determine confidence levels, and measure goodness of fit with respect to the predictive insights.
• Level 2: Optimized Human-decision Making. The second level of advanced analytics seeks to uncover and quantify the customer, product, and operational insights (predicted behavioral and performance propensities) buried in the data. Level 2 leverages Predictive and Prescriptive analytics that can uncover and codify individualized trends, patterns, and relationships; determine the root causes of the trends, patterns and relationships, and deliver dynamic, prescriptive recommendations and actions.
• Level 3: The Learning and Intelligent Enterprise. The third level of advanced analytics includes artificial intelligence, reinforcement learning and deep learning/neural networks. Level 3 leverages Autonomous analytics that continuously learn and adapt with minimal human intervention. These analytics seek to model the world around them—based upon the objectives as defined in the AI Utility Function—by continuously taking action, learning from that action, and adjusting the next action based upon the feedback from the previous action. Think of this as a giant game of “Hotter and Colder” where the analytics are continuously learning from each action and adjusting based upon the effectiveness of that action with respect to the operational goals (maximize rewards while minimizing costs) with minimal human intervention.

There are a couple of different ways or uses cases for exploiting autonomous analytics.  One is the creation of autonomous devices and the other is the creation of autonomous processes.  Let’s review each.

Use Case #1: Autonomous Devices

Okay, I have certainly beaten this topic to death, but it bears repeating because it is such a game changer for any organization that sells products (or products as a service).  Tesla is exploiting its ever-growing body of operational and driving data to continuously train the autonomous Tesla Artificial Intelligence-based Full Self-Driving (FSD) brain powers the Tesla semi-autonomous car.  Tesla mines this data to uncover and codify operator, product, and operational insights then get propagated back to each individual car resulting in continuously-refining and adapting capabilities such passing cars on the highway, navigating to the off ramp, maneuvering around traffic accidents and debris on the roads, and parking.  

Tesla autonomous cars are exploiting the capabilities of AI to create continuously learning autonomous cars that get more reliable, more efficient, safer, more intelligent, and consequently more valuable through usage…with minimal human intervention (Figure 2).

Figure 2: How AI is Creating Autonomous Devices

Tesla is not alone in building autonomous products.  John Deere is building autonomous farm tractors, Caterpillar is building autonomous construction equipment, and Nuro is building autonomous delivery vehicles because you gotta get that pizza delivered on time. Heck, one can’t be considered a serious industrial company if you don’t have a plan for creating autonomous products.

Use Case #2: Autonomous Policies

As operations become more complicated and more real-time, it’s becoming harder for organizations to ensure that their operating policies and procedures are evolving as fast as their business and operating environments. Digitalization provides a golden opportunity to improve operational effectiveness by replacing human-centric analog tasks with digital capabilities. That not only reduces human time and expense but allows organizations to capture more real-time, granular data about customer usage patterns and product performance characteristics.

This is the perfect time for leveraging AI to create autonomous policies, and procedures that can evolve at the speed of the business…with minimal human intervention. This evolution to autonomous policies and procedures starts by replacing code-based procedures and policies with AI-based learning-based procedures and policies (Figure 3).

Figure 3: “Leverage AI to Create Autonomous Policies”

Using AI, we can transition from static policies to autonomous policies that learn how to map any given situation (or state) to an action to reach a desired goal or objective with minimal human intervention. These autonomous policies would dynamically learn and update in response to constantly changing environmental factors (such as changes in weather patterns, economic conditions, price of commodities, trade and deficit balances, global GDP growth, student debt levels, fashion trends, Cubs winning the World Series, etc.).

Autonomous policies and procedures not only can ensure that the organization is making informed business and operational decisions, but can also combat bias, prejudice, and discrimination in making decisions. For example, Malcolm Gladwell’s “Talking to Strangers” highlights how AI-informed decisions can lead to equitable decisions in the judicial system.

Economist Sendhil Mullainathan examined 554,689 bail hearings conducted by judges in New York City between 2008 and 2013. Of the more than 400,000 people released, over 40% either failed to appear at their subsequent trials or were arrested for another crime. Mullainathan applied an ML program to the raw data available to the judges and the computer made decisions on whom to detain or release that would have resulted in 25% fewer crimes.

However, AI-driven policy decisions have their own challenges. As I discussed in “Ethical AI, Monetizing False Negatives and Growing Total Addressabl…”, AI model confirmation bias is the tendency for an AI model to identify, interpret, and present recommendations in a way that confirms the AI model’s preexisting assumptions. AI model confirmation bias feeds upon itself, creating an echo chamber effect with respect to the biased data that continuously feeds the AI models. Overcoming AI model confirmation bias starts by 1) understanding the costs associated with False Positives and False Negatives and 2) building a feedback loop where the AI model can continuously-learn and adapt from the False Positives and False Negatives.

Transitioning from Descriptive to Autonomous analytics is a game-changer but must be framed against the Data & Analytics Business Maturity Index to help organizations become more effective at leveraging data and analytics to power their business (Figure 4).

Figure 4: Data & Analytics Business Maturity Index

By the way, Jeff Frick does a marvelous job grilling me on the 8 Laws of Digital Transformation.  The video is a lot more interesting than this blog. Grab some Cap’n Crunch and enjoy the conversation!

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DSC Weekly Digest 20 July 2021

COVID-19 has been in the news again lately, for several reasons. In many parts of the world, the delta variant of the virus has been hitting hard in those areas where vaccination rates are low. Not surprisingly, these are also the areas where there is a broad mistrust of science and where local leaders have sewn that mistrust for their own political gain. This is in turn breeding resentment and denialism in those regions that have the potential to turn into a vicious cycle, exacerbating geopolitical tensions and widening economic inequality.

It is still possible, with the vaccine, to still get COVID-19 Delta. though the likelihood is much smaller and the effects (and transmissibility) of the virus considerably tempered. However, this does not mean that the virus (regardless of variant) has become less dangerous to those who haven’t been inoculated, and even those who have had COVID-19 are not necessarily immune to the delta variant, though at least some of the vaccines seem to be better at provoking a full spectrum response.

This is raising the specter of a forever virus, one that may very well take years to fully recede, as potentially dangerous mutations continue to build up in broad pockets of “low-science” regions. The longer that this goes on, the more that the very nature of work and society is likely to change. Already, companies that had begun to require employees to come back to the office full time are reassessing those policies in the light of rising case numbers, though at least at this time the potential of going back into a full lockdown mode seems unlikely – hospitalization and death rates are not rising as quickly as has been the case before.

One of the more intriguing aspects of the pandemic has been that it has forced companies into thinking hard about what exactly they do. The demand for more sophisticated AI systems is rising, but there are also multiple indications that the field of AI itself needs to evolve dramatically first, taking into account not just faster AIOps platforms but increasingly needing to integrate with contextual stores and provide more intuitive interfaces that can adapt dynamically (and automatically) to given needs.

These won’t come from better algorithms. Rather, AI itself needs to adapt more readily as a component within a broader matrix of services that organizations use. This will become especially important as work becomes increasingly virtual and distributed, and is a key reason why data scientists and modelers need to start thinking beyond the analysis and toward the applications that rely upon that analysis.

In media res,

Kurt Cagle
Community Editor,
Data Science Central

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