Machine learning and artificial intelligence are now moving from the realm of research into adoption. Machine learning adoption offers immense benefits which can provide any organization with a competitive edge — if executed well. Technological adoption requires a pragmatic and collaborative approach across the organization driven by agile practices.This also comes the need for trusted data sources, organizational change management, iterative revalidation practices and measuring the business value of the technology insertion.

In part one of this series on machine learning (ML), we defined machine learning, delved further into the various types of machine learning models, and described their common applications. This article focusses on the tactical execution steps and organizational modifications required to make the ML dream a reality.

5 Steps to Machine Learning Implementation

Establishing machine learning within any organization requires planning and collaboration. As with any technology insertion and/or transition, it starts with a vision and moves on to execution followed by continuous monitoring and improvement. The basic steps to building an ML implementation plan are described in five simple steps below: VDOCR — Vision, Data, Organizational alignment, Change management and Revalidation.

1: Establish a Vision

Establishing a vision is perhaps the most important step in implementing a new technology. It is not any different for machine learning. Business and IT must work together to establish a vision and define clear objectives for an ML implementation. The objectives could be as simple as improving the accuracy of the fraud detection system all the way to improving overall operational efficiency — but it needs business and IT alignment and the agreement to work towards a common goal.

Without a clear understanding of what you want to achieve, it’s hard to measure success. You’ll find the most common use cases by looking for places that are labor intensive and repetitive such as image classification, tuning/optimizing your data center operations, configuration management, and systems patching/updating. This step also includes establishing key performance indicators to measure the business value of the program.

2: Define Data Requirements

Data is perhaps the single most important element required for the success of a machine learning implementation. Collecting, storing and feeding the system vast amounts of reliable data is the key to improving the accuracy of machine learning algorithms. Data management processes need to be established for:

• Providing an initial set of historical data to train the ML processing system.
• For continuous data insertion to train and improve the accuracy of the model.

Beyond the initial model-training phase, infrastructure will be needed to collect new data from which to learn over time. Data requirements need to be established not only for collecting and storing data but also to ensure that the available data is reliable and secure and is available in a steady stream for continuous improvement.

3: Establish Roles and Responsibilities

Any successful technology implementation requires integration across the organizational landscape that is strategically led by a robust management function, clear establishment of roles and responsibilities and cultural integration. Begin with the creation of integrated solution teams with representatives from IT, marketing, sales, and other required stakeholders that meet regularly during the project to review progress and ensure adequate coordination with their respective groups.

4: Set Up a Change Management Process

Technology insertions often fail due to the lack of adequate change management processes. Change management and training are two of the key aspects of delivery and acceptance of any large-scale modernization effort, and ML implementation is no different in that respect. Change management includes looking at current business processes and re-engineering them based on the updated business model. In addition, training programs that cover mission objectives, product features as well as the newly created business processes are imperative to create collective support and awareness for the mission and its objectives as well as to increase efficiency and use.

5: Establish Monitoring and Revalidation

Gauging the success of an application and whether it needs changes can be established by measuring its business value. To ensure that ML models remain relevant and ultimately result in business value, they need to be continuously updated, retrained and validated. To achieve this, organizations need to ensure that any ML implementation plan includes the ability to update its criteria based upon evaluated outcomes and to incorporate improved and increasing amounts of data. Also important is to measure how the ML algorithm affects broader business goals.

For example, Amazon is continuously refining its prediction algorithms based on the past purchases of its customers. Similarly, Netflix improves its ability to provide customized content to its consumers based on the content they watch. Moreover, New York Times has even developed an ML system to ascertain the emotions evoked by news articles with the goal of helping advertisers’ places ads more effectively.

Imagine an article that changes its content based on what its consumers wants to read or a movie that changes its story based on the likes and dislikes of its viewers. Sounds eerie! Get ready for it — because it is coming ….

Get Started With Your Machine Learning Strategy

Businesses need to carefully plan and manage technology disruptions and ML is no different in that respect. If you want to get the most out of your business data and automate processes, the time is ripe for creating an ML strategy in your organization. Following the simple VDOCR (Vision, Data, Organization, Change and Revalidation) model will help your organization take its first step towards an ML implementation that considers cultural implications and delivers business value.

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In part 2 of our machine learning tutorial, learn how to use the supervised learning approach to machine learning to produce the best predictions.

The following article is comprised of excerpts from the course “Fundamental Machine Learning” that is part of the Machine Learning Specialist certification program from Arcitura Education. It is the second part of the 13-part series, “Using machine learning algorithms, practices and patterns.”

Supervised learning is one of the most widely used machine learning approaches. It can be useful for predicting financial results, detecting fraud, recognizing objects in images and evaluating or assessing risk. The aim of supervised learning is to allow machine learning functions to work in such a way that enables the input data to be used to predict the output class for each new data instance for which the classification is not already known.

With supervised learning, the input data and output data (also called the class) are known in advance. This allows the model to be trained so that it produces the best predictions of classes for the training data by knowing when the model did and did not make a classification error (Figure 1). Subsequent to training the model with the labeled data set, the trained model can then be used to classify future input data with unknown classification.

As part of a supervised learning process, the machine learning system is required to identify circles in images from the data it receives. The result is a form of predictive modeling, whereby the machine recognizes the circles from the other shapes in images (Figure 1).

As the machine learning system continues to make decisions based on the data presented to it, the results of its decisions are reviewed (supervised) by the algorithm. When incorrect decisions are made during training with the labeled data, the algorithm has the opportunity to make adjustments as part of the training process .

Once the model is trained and deployed, the machine learning system can make decisions based on new data it processes.

The following are common supervised learning algorithms, algorithm types and practices:

• Classification
• Decision tree
• Regression
• Predictive modeling
• Ensemble models and methods

Read on to learn more about these five algorithms.

Classification

A problem is referred to as a classification when the output is expected to be a category — such as a circle, a color, a type of car or an outcome. Take, for example, a problem that asks Based on historical weather data, will it rain on February 12? If the model predicts two class values, then the output could be rain and no rain. A classifier would not produce the probability percentage for rain, such as 90% chance of rain. For these types of problems several different classification algorithms and techniques can be used (Figure 4).

When the model is trained, different labels are assigned to different data classes (such as circles and triangles) to enable the model to determine which category the data belongs to.

Where classifications can help

To understand how classification algorithms work in machine learning, consider this hypothetical business case.

A toy company needs to determine how to distribute its inventory of toys across retail locations in different countries. It cannot give each retail store the same inventory because it has learned, over the years, that some types of toys consistently sell better in certain regions than others. Some toys, in particular, hardly sell at all in some regions.

They use a classification algorithm to mine the transaction records in their database as labeled examples in order to classify toy types for each region:

• Class A: types of toys that sell at a poor rate in this region.
• Class B: types of toys that sell at an average rate in this region.
• Class C: types of toys that sell at an exceptional rate in this region.

Once the classifier is trained, all new toys types are classified into one of the three classes for each region. Finally, the toy company proceeds to set inventory quantities for the stores in the different regions using this classification system. Toys that fall under Class A are stocked in modest quantities. Toys that fall under Class B are stocked at standard quantities. Toys that fall under Class C are stocked at high quantities.

The toy company uses a further classification algorithm specifically for toys that fall under Class A to break down those toys into additional classes. They study this information to try to better understand what may have been leading to poor sales in the past. For example, causes of poor sales may have been due to inaccurate marketing, regional competitors offering similar toys at a lower cost or cultural incompatibility with some types of toys.

Decision tree

Decision tree algorithms are a form of classification algorithms that use rules organized into a flowchart. The rules are carried out sequentially so that once one decision is made, the algorithm moves on to the next

When a decision tree approach works

If our hypothetical toy company wanted to establish a new feature for its online shopping site, it might use the decision tree approach. When a customer logs in and begins browsing the store, the site needs to be able to provide recommendations of toy products that match the customer profile information.

A decision tree algorithm is developed to narrow down which products to display by carrying out a sequence of decisions. It first decides whether the customer previously purchased a toy over $100 within the past 30 days. If the customer did, it then determines whether the customer has previously purchased toys for boys or girls or both. Based on the outcome of that decision, the algorithm produces a classification which is used to choose one or more products to display on the website while the customer is browsing.

For example, the leaf classifying customers as the type to make a recent purchase over $100 for a boy (named Class_Recent_Purchase_Boy by the analyst) would be presented with the top three best-selling toys for boys on the webpage.

The leaf classifying customers as the type of customer to make a recent purchase over $100 for a girl (named Class_Recent_Purchase_Girl by the analyst) would be presented with the top three best-selling toys for girls on the webpage.

Another leaf would further classify customers who buy toys for both genders (i.e.,  toys that are gender neutral) and customers who spent less than $100 within in the last 30 days. For these customers, generic recommendations are presented on the webpage.

Regressions  

Regression is a method of modeling a target value based on independent predictors. A regression algorithm attempts to estimate the relationship across variables (Figure 6). Take, for example, the problem asked above: Based on historical weather data, what will the high temperature likely be on February 12?

Regression analysis is mostly used for tasks such as forecasting and determining cause and effect relationships. They are used when the results are expected to be quantifiable, such as a length, width, age and so on.

What a regression algorithm reveals

What if our hypothetical toy company is planning its marketing campaign for a new line of products. As part of the marketing campaign, it wants to send out promotional emails to its previous customers. Instead of sending out one email to all customers with information about all of its new products, it would like to customize the emails so they promote types of toys that certain customers are more likely to purchase.

They use a regression algorithm to match categories of toys with customer profiles to produce probabilities of certain types of customers purchasing certain types of toys. Historical customer transaction data is used to determine the types of toys a customer previously purchased, how frequently those types of toys were purchased and how much, on average, the customer spent on those types of toys. This information is used to generate the probability of a customer purchasing a new toy of the same type at a given price point.

For example, the algorithm determines that a customer who had previously purchased action figure toys five times over the past six months and who spent an average of $80 on each purchase is 71% likely to purchase a new action toy of the same type that is priced at $40.

The purchase probability of other toys is also assessed and compared, and the promotional email sent to that customer is customized to highlight the new toys with at least a 60% purchase probability at the toy’s current price.

Ensemble methods

Ensemble methods help to improve the outcome of applying machine learning by combining several learning models instead of using a single model. The ensemble learning approach results in better prediction compared to when using a single learning model.

Some industry experts refer to algorithms used in ensemble methods as meta algorithms because they combine several learning techniques into one single model and use a complex method of prediction.

How ensemble methods can help

What if the toy company has a limited number of toy samples for a new line of toys it is releasing, and it would like to send the samples only to high-value customers to reward them for their loyalty? It is challenging to determine which customers should be valued more highly than others.

The company uses an ensemble method that will be applied to a combination of models, each of which represents a factor in determining the customer’s value to the company. The following models are identified:

• total amount spent during all transactions to date;
• length of time the customer has made purchases; and
• whether the types of previous purchases are the same toy type as the sample.

The ensemble method factors in all three models. Ensemble voting is then used to determine whether the customer is considered high-value and will, therefore, receive the toy sample.

An ensemble model is the result of applying different ensemble methods. Ensemble methods can be used for stacking (improving prediction), boosting (bias) and bagging (decreasing the variance) purposes.

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Researchers have developed a system to identify drugs that might be repurposed to fight the coronavirus in elderly patients

When the COVID-19 pandemic struck in early 2020, doctors and researchers rushed to find effective treatments. There was little time to spare. “Making new drugs takes forever,” says Caroline Uhler, a computational biologist in MIT’s Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society, and an associate member of the Broad Institute of MIT and Harvard. “Really, the only expedient option is to repurpose existing drugs.”

Uhler’s team has now developed a machine learning-based approach to identify drugs already on the market that could potentially be repurposed to fight COVID-19, particularly in the elderly. The system accounts for changes in gene expression in lung cells caused by both the disease and aging. That combination could allow medical experts to more quickly seek drugs for clinical testing in elderly patients, who tend to experience more severe symptoms. The researchers pinpointed the protein RIPK1 as a promising target for COVID-19 drugs, and they identified three approved drugs that act on the expression of RIPK1.

The research appears today in the journal Nature Communications. Co-authors include MIT PhD students Anastasiya Belyaeva, Adityanarayanan Radhakrishnan, Chandler Squires, and Karren Dai Yang, as well as PhD student Louis Cammarata of Harvard University and long-term collaborator G.V. Shivashankar of ETH Zurich in Switzerland.

Early in the pandemic, it grew clear that COVID-19 harmed older patients more than younger ones, on average. Uhler’s team wondered why. “The prevalent hypothesis is the aging immune system,” she says. But Uhler and Shivashankar suggested an additional factor: “One of the main changes in the lung that happens through aging is that it becomes stiffer.”

The stiffening lung tissue shows different patterns of gene expression than in younger people, even in response to the same signal. “Earlier work by the Shivashankar lab showed that if you stimulate cells on a stiffer substrate with a cytokine, similar to what the virus does, they actually turn on different genes,” says Uhler. “So, that motivated this hypothesis. We need to look at aging together with SARS-CoV-2 — what are the genes at the intersection of these two pathways?” To select approved drugs that might act on these pathways, the team turned to big data and artificial intelligence.

The researchers zeroed in on the most promising drug repurposing candidates in three broad steps. First, they generated a large list of possible drugs using a machine-learning technique called an autoencoder. Next, they mapped the network of genes and proteins involved in both aging and SARS-CoV-2 infection. Finally, they used statistical algorithms to understand causality in that network, allowing them to pinpoint “upstream” genes that caused cascading effects throughout the network. In principle, drugs targeting those upstream genes and proteins should be promising candidates for clinical trials.

To generate an initial list of potential drugs, the team’s autoencoder relied on two key datasets of gene expression patterns. One dataset showed how expression in various cell types responded to a range of drugs already on the market, and the other showed how expression responded to infection with SARS-CoV-2. The autoencoder scoured the datasets to highlight drugs whose impacts on gene expression appeared to counteract the effects of SARS-CoV-2. “This application of autoencoders was challenging and required foundational insights into the working of these neural networks, which we developed in a paper recently published in PNAS,” notes Radhakrishnan.

Next, the researchers narrowed the list of potential drugs by homing in on key genetic pathways. They mapped the interactions of proteins involved in the aging and SARS-CoV-2 infection pathways. Then they identified areas of overlap among the two maps. That effort pinpointed the precise gene expression network that a drug would need to target to combat COVID-19 in elderly patients.

“At this point, we had an undirected network,” says Belyaeva, meaning the researchers had yet to identify which genes and proteins were “upstream” (i.e. they have cascading effects on the expression of other genes) and which were “downstream” (i.e. their expression is altered by prior changes in the network). An ideal drug candidate would target the genes at the upstream end of the network to minimize the impacts of infection.

“We want to identify a drug that has an effect on all of these differentially expressed genes downstream,” says Belyaeva. So the team used algorithms that infer causality in interacting systems to turn their undirected network into a causal network. The final causal network identified RIPK1 as a target gene/protein for potential COVID-19 drugs, since it has numerous downstream effects. The researchers identified a list of the approved drugs that act on RIPK1 and may have potential to treat COVID-19. Previously these drugs have been approved for the use in cancer. Other drugs that were also identified, including ribavirin and quinapril, are already in clinical trials for COVID-19.

Uhler plans to share the team’s findings with pharmaceutical companies. She emphasizes that before any of the drugs they identified can be approved for repurposed use in elderly COVID-19 patients, clinical testing is needed to determine efficacy. While this particular study focused on COVID-19, the researchers say their framework is extendable. “I’m really excited that this platform can be more generally applied to other infections or diseases,” says Belyaeva. Radhakrishnan emphasizes the importance of gathering information on how various diseases impact gene expression. “The more data we have in this space, the better this could work,” he says.

This research was supported, in part, by the Office of Naval Research, the National Science Foundation, the Simons Foundation, IBM, and the MIT Jameel Clinic for Machine Learning and Health.

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Dr. V.N. Manjunath Aradhya, Associate Professor and Head, Department of Computer Applications, JSS Science and Technology University, Mysuru, has developed a model for detecting COVID-19 from chest X-ray images.
This concept has an advantage of learning from a few samples. The model proposed is a multi-class classification model as it classifies images of four classes — pneumonia bacterial, pneumonia virus, normal, and COVID-19. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The proposed concept is the first-of-its-kind in the literature and expected to open up several new dimensions in the field of machine learning.

This research article was recently accepted in one of the top tier Journal, Cognitive Computation, Springer. This work is a combined effort with Prof. D. S. Guru of University of Mysore (UoM) and Prof. Mufti Mahmud of Nottingham Trent University, UK. Recently, Dr. Aradhya also published papers on understanding and analysis of COVID-19 which is co-authored with Prof. G. Hemantha Kumar, Vice-Chancellor, UoM, according to a press release from Dr. S. A. Dhanaraj, Registrar of the University.

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