It's now time to conclude our discussion on automation with a short introduction to Automated ML or AutoML in the final section. Summary In the past decade, deep learning has made incredible progress. Every as Auto Keras, Auto WEKA, NNI and auto-sklearn have gone even further and adopted the doctrine of AutoML which aims to automate most of the steps involved in the machine learning pipelines to reduce the the dependency on ML experts and to promote large-scale adoption of machine learning. An AutoML pipeline assumes all the responsibilities which traditionally required ML experts. Imagine that we are developing

SparkBench  Array Databases: GenBase  #3 Machine Learning Systems  SLAB, DAWNBench, MLPerf, MLBench, AutoML Bench, Meta Worlds, TPCx-AI Experiments and Result Presentation [http://www.tpc.org/tpch/] (See