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TDWI Upside - Where Data Means Business

The Rise of Automated Machine Learning

No matter what industry you're in, autoML can help you use machine learning successfully and extract and leverage business insights hidden in places where only machine learning can reach.

Machine learning (ML) has a rapidly increasing presence across industries. Top technology companies such as Amazon, Google, and Microsoft certainly talked a lot about ML's big impact on powering applications and services in 2017. Its usefulness continues to emerge in businesses of all sizes: automatically targeting segments of the market at marketing agencies, e-commerce product recommendations and personalization by retailers, and fraud prevention customer service chatbots at banks are examples.

For Further Reading:

Machine Learning in 2019: Getting it Right from the Cloud to the Edge

Humans in the Loop for Machine Learning

Machine Learning that Automates Data Management Tasks and Processes

Certainly ML is a hot topic, but there's another related trend that's gaining speed: automated machine learning (autoML).

What is Automated Machine Learning?

The field of autoML is evolving so quickly there's no universally agreed-upon definition. Fundamentally, autoML offers ML experts tools to automate repetitive tasks by applying ML to ML itself.

A recent Google Research article explains that "the goal of automating machine learning is to develop techniques for computers to solve new machine-learning problems automatically, without the need for human-machine learning experts to intervene on every new problem. If we're ever going to have truly intelligent systems, this is a fundamental capability that we will need."

What's Driving Interest

AI and machine learning require expert data scientists, engineers, and researchers, and there's a worldwide short supply right now. The ability of autoML to automate some of the repetitive tasks of ML compensates for the lack of AI/ML experts while boosting the productivity of their data scientists.

By automating repetitive ML tasks -- such as choosing data sources, data prep, and feature selection -- marketing and business analysts spend more time on essential tasks. Data scientists build more models in less time, improve model quality and accuracy, and fine-tune more new algorithms.

AutoML Tools for Citizen Data Scientists

More than 40 percent of data science tasks will be automated by 2020, according to Gartner. This automation will result in the increased productivity of professional data scientists and broader use of data and analytics by citizen data scientists. AutoML tools for this user group usually offer a simple point-and-click interface for loading data and building ML models. Most autoML tools focus on model building rather than automating an entire, specific business function such as customer analytics or marketing analytics.

Most autoML tools (and even most ML platforms) don't address the problem of data selection, data unification, feature engineering, and continuous data preparation. Keeping up with massive volumes of streaming data and identifying non-obvious patterns is a challenge for citizen data scientists. They are often not equipped to analyze real-time streaming data, and if data is not analyzed promptly, it can lead to flawed analytics and poor business decisions.

AutoML for Model Building Automation

Some companies are using autoML to automate internal processes, particularly building ML models. A few examples of companies using autoML for automating model building are Facebook and Google.

Facebook trains and tests a staggering number of ML models (about 300,000) every month. The company essentially built an ML assembly line to deal with so many models. Facebook has even created its own autoML engineer (named Asimo) that automatically generates improved versions of existing models.

Google is developing autoML techniques for automating the design of machine learning models and the process of discovering optimization methods. The company is currently developing a process for machine-generated architectures.

AutoML for the Automation of End-to-End Business Processes

Once a business problem is defined and the ML models are built, it's possible to automate entire business processes in some cases. It requires appropriate feature engineering and pre-processing of the data. Examples of companies actively using autoML for the whole automation of specific business processes include DataRobot, ZestFinance, and Zylotech.

DataRobot is designed for the whole automation of predictive analytics. The platform automates the entire modeling lifecycle which includes, but is not limited to, data ingestion, algorithm selection, and transformations. The platform is customizable so that it can be tailored for specific deployments such as building a large variety of different models and high-volume predictions. Data Robot helps data scientists and citizen data scientists quickly build models and apply algorithms for predictive analytics.

ZestFinance is designed for the whole automation of specific underwriting tasks. The platform automates data assimilation, model training and deployment, and explanations for compliance. ZestFinance employs machine learning to analyze traditional and nontraditional credit data to score potential borrowers who may have thin or no files. AutoML is also used to provide tools for lenders to train and deploy ML models for specific use cases such as fraud prevention and marketing. ZestFinance helps creditors and financial analysts make better lending decisions and risk assessments.

Zylotech is designed for the whole automation of customer analytics. The platform features an embedded analytics engine (EAE) with a variety of automated ML models, automating the entire ML process for customer analytics, including data prep, unification, feature engineering, model selection, and discovery of non-obvious patterns. Zylotech helps data scientists and citizen data scientists leverage complete data in near real time that enables one-to-one customer interactions.

AutoML Helps Businesses Use Machine Learning Successfully

You've probably heard the phrase "data is the new oil." It turns out data is now far more valuable than oil. However, just as crude oil needs to be "cracked" before it is turned into useful molecules, customer data must be refined before insights can be drawn from it with embedded models. Data is not instantly valuable; it must be collected, cleansed, enriched, and made analysis ready.

The autoML approach can help all businesses use machine learning successfully. Potential business insights are hidden in places where only machine learning can reach at scale. Whether you're in marketing, retail, or any other industry, AutoML is the methodology you need to extract and leverage that valuable resource.

About the Author

Abhi Yadav is the founder and CEO of Cambridge, MA-based Zylotech, a self-learning customer analytics platform that keeps customer data live and enriched while automating the customer life cycle with relevance to continuously produce cross-selling, up-selling, and retention marketing results.

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