Machine learning is moving beyond the hype


Machine learning has been around for decades, but for much of that time, businesses were only deploying a few models and those required tedious, painstaking work done by PhDs and machine learning experts. Over the past couple of years, machine learning has grown significantly thanks to the advent of widely available, standardized, cloud-based machine learning platforms.

Today, companies across every industry are deploying millions of machine learning models across multiple lines of business. Tax and financial software giant Intuit started with a machine learning model to help customers maximize tax deductions; today, machine learning touches nearly every part of their business. In the last year alone, Intuit has increased the number of models deployed across their platform by over 50 percent.

In another example, rideshare leader Lyft collects massive amounts of data in real time from the mobile apps of more than two million drivers and 30 million riders. The company uses millions of machine learning models to accurately detect anomalies in route usage or driving patterns that could signal problems that require immediate attention.

But this is just the beginning. The next phase of machine learning will deliver what scientists could only dream of: industrializing and democratizing machine learning. With purpose-built machine learning platforms and tools that can systematize and automate deploying machine learning models at scale, we’re on the cusp of a major shift that will make it possible for all enterprises—not just the global Fortune 50 companies—to use this transformative technology and become truly disruptive. 

The path to machine learning industrialization

Machine learning is following a familiar trend seen repeatedly across industries: using automation to industrialize processes and achieve mass deployment. The first autos, for example, were designed by boutique manufacturers such as Duryea and Packard who produced fanciful luxury vehicles in limited production because they required tedious, painstaking work. The Ford Motor Company turned that idea on its head by standardizing auto design and manufacturing processes to create an assembly line, enabling mass consumption of the automobile, changing transportation and commerce forever.

Nine decades later, the software industry underwent a similar transformation from a collection of elegant, bespoke applications developed by a few specialized coders into a systematic engineering discipline that is now broadly accessible. Today, integrated development environments, debuggers, profilers, and continuous integration and continuous deployment (CI/CD) tools provide standardization and automation of software development that enable coders at all levels to create robust applications. The ability to mass-produce applications has in turn, driven mass-consumption of software and made software integral to how we live and work.

Copyright © 2021 IDG Communications, Inc.



Source link