According to Research and Markets, the global AutoML market is expected to touch $15 billion market cap by 2030, from $270 million in 2019.
Are you thinking of learning programming languages like C++, Python or R to work on machine learning projects? AutoML could save you all the time and effort.
Can AutoML Outperform Data Scientists: A New Survey. Automated machine learning – or AutoML – was introduced to fill in for the talent gap in the ML industry and eliminate the mundane tasks of ML engineers.
We’ll be referring back to this specific notebook as we test drive AutoML solutions, so you might want to create a copy and run through it — clicking on the image above will take you directly to the notebook.
AutoML is no longer a new term. Since Google released its first AutoML product in 2018, discussions around this technology have been quite prominent. Some regard it as a weapon to achieve general artificial intelligence, while others deem it to be exaggerated. But what everyone agrees on is that AutoML does have extraordinary significance in…
My readers selected AutoML as the trend that will impact their job/ industry in the short time the most. What is it and why should you care?
EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python. EvalML provides a simple, unified interface for building machine learning models, using those models to generate insights and to make accurate predictions. EvalML provides access to multiple modeling libraries under the same API. EvalML supports a variety of supervised machine learning problem types including regression, binary classification and multiclass classification. Custom objective functions let users phrase their search for a model directly in terms of what they value. Above all we’ve aimed to make EvalML stable and performant, with ML performance testing on every release.