Automated machine learning, also referred as AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development.
With automated machine learning users accelerate the time it takes to get production-ready ML models with great ease and efficiency.
According to Wayne Butterfield, AutoML is to ‘no data scientists’ what robotic process automation is to ‘no developers.’
It is the low-code/no-code equivalent of a useful tool that would otherwise require a skill set the masses do not possess.
He explained that training neural network models is an automated process where data is continuously fed as input to the model.
State-of-the-art commercial machine learning systems now adopt ML Ops best practices, which ensures that the entire workflow from raw data to selected models (and even model deployment) is fully reproducible from start to finish.
“Any commercial organization, particularly ones with enterprise customers, involved in developing machine learning products need to take ML Ops extremely seriously and they ought to invest heavily in these systems.”
The benefit will be that machine learning engineers will identify problems with their production models before customers do, and they will be enabled to efficiently react to those issues, adjust those models either by just adding more data to the training, or modifying the training protocol slightly and deploy regular improvements to those models to maintain optimal user experience.
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