This presentation gives a broad overview of AutoML, ranging from simple hyperparameter optimization all the way to full pipeline automation.
After going over the theoretical framework and explanation of AutoML, he will dive into concrete examples of different types of AutoML.
Danny will leverage Apache Spark (a framework popular with data scientists who need to scale machine learning workloads to Big Data) and Apache Zeppelin notebooks, as well as popular Python libraries such as Pandas, Plotly and bayes-opt.
Data science experts and novices alike will find this presentation accessible and enlightening.
Participants will receive in-depth knowledge of hyperparameter tuning (using grid search, random search, Bayesian optimization, and genetic algorithms) and will be exposed to new tools for automating machine learning workflows.
The slides for this presentation are available HERE.
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