(Jeremy Vale coauthored this post.)
Automation-focused machine learning (AutoML) has the power to dramatically upscale AI at your organization. With AutoML tools, organizations can unlock valuable new business insights, embed advanced AI capabilities in applications, and empower data scientists and nontechnical experts alike to build predictive models rapidly.
AutoML Gives New ML Powers To Data Scientists
AutoML automates repetitive, tedious, and time-intensive tasks that eat up a lot of data scientists’ time. Endowed with this technology, your super data scientists can iterate faster, try more features and algorithms, and tackle more priority projects. New superpowers, like the ability to build deep learning models for image recognition and natural language understanding, once the exclusive purview of a select few data scientists, will be in reach for the many.
Organizations around the world see the appeal. In the Forrester Analytics Global Business Technographics® Data And Analytics Survey, 2019, 61% of data and analytics decision makers whose firms are adopting AI said they had implemented, were in the process of implementing, or were expanding/upgrading their implementation of automation-focused machine-learning solutions. Another 25% planned to implement within the next year.
An Army Of Super Allies And Sidekicks
Not only does AutoML empower your data scientists, but it also augments their allies and sidekicks. With AutoML added to their trusty tool belt, anyone can build their own predictive model with ease — whether they have even basic data science skills or not. Providing a widely available internal platform to democratize predictive analytics will pay dividends. AutoML’s simplified processes allow for initial experimentation to be the work of anyone at your organization. Senior executives can leverage it to get the insights they need fast. Nontechnical domain experts can have their hand in model development without entering a single line of code. Of course, disaster can strike when sidekicks decide to go it alone — nondata scientists should always receive training and oversight from the experts for quality assurance.
With Great ML Power Comes Great Responsibility
Naturally, no superhero is complete without its Kryptonite. AutoML will not automatically create business value if it isn’t used for the suitable use cases and if it isn’t used correctly. It can’t do all of the advanced analytics and machine learning that your organization demands. It won’t handle the most complex or critical projects on its own. An in-depth understanding of AutoML’s strengths and weaknesses is critical if it is to be utilized correctly.
With an increasingly robust ecosystem of AutoML vendors (see our Q2 2019 evaluation of nine AutoML solutions providers), superherolike abilities for you and your organization are at your fingertips — no radiation, mutation, or superbugbite required. Enable your data science team to achieve its full potential and marvel at the results. To learn more about maximizing the value of this exciting emerging technology through seven key questions, check out our full report, “Q&A: Scale AI With Automated Machine Learning.”