Real-World AI/ML Use Cases - Part #3 in the Intro to AI/ML Series

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Based on MapR Academy course, BUS Introduction to Artificial Intelligence and Machine Learning.

This blog is third in the Intro to AI/ML Series. You can read Part 1 here and Part 2 here.

Machine learning can be applied to any scenario where data is analyzed. ML is used in every industry to gain a strong business advantage by minimizing overhead costs, predicting user activity, and discovering new insights and untapped opportunities in each market.

From retail to finance, healthcare, manufacturing businesses, and much more, industries are all taking advantage of using artificial intelligence in their data science efforts.

Use Case: Machine Learning in Retail

Retail businesses use machine learning for a wide variety of purposes in many markets. In e-commerce, they track shopping cart activity and avert abandonments, provide useful product recommendations, plan targeted promotions, and forecast product demand for stock availability.

Their brick-and-mortar stores effectively apply ML to track customer product interest and spend in real time. Both markets integrate seamlessly together to provide a fully personalized, holistic customer 360 program.

All these efforts combine to offer their business reduced administration costs and increased profits, all while providing a personalized touch for every customer.

A very popular use of ML that we're all familiar with is the recommender system. We've all made purchases online and have received recommendations for related items. These recommendation engines use an information filtering technique that predicts our preference for, or rating of, an item.

As with many things in machine learning, this can be applied to just about anything, from products like clothing, books, movies, or music, to research articles, jobs, restaurants, online dating, and Twitter feeds.

Use Case: ML in Finance

In financial services, we have a consumer credit card company that uses machine learning for credit card fraud detection and credit line approval. Both of these are considered binary classification problems, giving a result of fraud or not fraud, or approved or declined. Specific promotional and offer recommendations are also made to their customers, based on previous purchase activity.

Finally, ML is used in a customer retention program, which we will look at in more detail, next.

Customer Retention Program

A credit card company uses ML in an attempt to predict at-risk customers and retain those who call in to close their accounts. Based on monitoring transaction activity and user demographic data, they can predict behavior and prepare offers specifically for these customers. This process uses a predictive, binary classification model to determine which customers are at risk, and then uses a classic recommender model to determine other suitable card offers that might retain these customers.

Note that in this example, multiple models are working together to solve this business problem.

Use Case: ML in Healthcare

In the healthcare industry, this provider uses machine learning to predict patient readmittance probability. Not only does this help save the time of medical professionals, but also allows them to provide better care for their patients. ML is used to perform early screens on patients for cancer, using anomaly detection and image recognition on ultrasounds and scans.

Medical Scan – Image Recognition Example

In this example, high-resolution lung scans are examined for lesions, using anomaly detection and image recognition.

Here, we can see how image recognition identifies lung segmentation, revealing lesions which are then evaluated by a professional for malignancy. In this way, human doctor time is used much more efficiently, as they don't have to sift through all image scans on their own, and they only have to focus on the scans showing potential issues.

Not only does machine learning assist these medical professionals by providing an additional pair of tireless eyes, but it has demonstrated constant improvements in accuracy and detection capabilities in the field.

Use Case: ML in Manufacturing

Manufacturing companies can use machine learning to analyze their data with the goal of finding efficiency improvements and improving quality control. Or, they can use ML to avert potential employee churn for cost reduction and also to analyze real-time streaming data from sensors to instantly assess and detect the need for machine line maintenance.

In this example, we see a company using anomaly detection on real-time streaming data to analyze IoT sensors on a robotic arm. It instantly assesses and detects potential equipment maintenance requirements. If the organization can accurately predict when a piece of hardware will fail, and replace that component before it fails, it saves them production costs, reduces downtime, and increases operational efficiency.

With so many devices now, including sensor data and components that send diagnostic reports, predictive maintenance using ML is becoming increasingly more accurate and effective.

Use Case: ML for the Self-Driving Car

Who isn’t excited about the concept of a self-driving car? Every day, this gets closer and closer to our daily reality. Machines – well, cars in this case – need to be trained on many different things before they can actually hit the road. Things like how to drive and what possible environments and conditions they may encounter, to start with.

Much of this can be achieved through basic supervised learning, training that requires vast amounts of labeled data.

Once perfected, this technology will need to be extremely intelligent, fully autonomous, self-organizing, and dynamic. But for now, let’s just start with the basics. How do we train a car to drive itself safely?

In this example, supervised learning techniques are used to train the machine through specific driving examples on speed and the conditions of terrain. Thousands of miles of driving are recorded and fed in as streaming input data in order to provide examples on the most appropriate driving methods for these varying conditions. This process is very human intensive, requiring hundreds of hours of driving to generate the input data, but the trainers know exactly what is being fed in and can control the details of what they want to train and classify as “good driving.”

This raw data will determine a linear decision surface which is displayed in a visual diagram, as shown in this scatter plot example. Based on this information, the car can detect the speed and the conditions of the terrain, and then determine how to best drive in that given scenario.

Keep in mind that this is a very simplistic view of the very beginning of the training process for a self-driving car and there are many other ways to accomplish this goal. The end product will be vastly more complex than what we see here.

Where do we go from here?

Next in this series, we'll see how MapR solutions can help with your AI/ML journey.

This blog post was published October 08, 2018.

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