AutoML in Practice

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The compelling Oct. 15, 2019 presentation below is on behalf of one of my favorite Meetup groups: LA Machine Learning. The talk, “AutoML in Practice,” is by Danny D. Leybzon, a Solutions Architect at Qubole, a cloud-native big data platform. Before joining the Solutions team, he was the Data Science Product Manager at Qubole. Danny has an academic background in computational statistics. He believes that good data science requires good data engineering in order to create clean, accurate, and accessible data for data scientists. In the past, he’s given presentations on distributed deep learning, productionizing machine-learning models, and the importance of big data for machine learning in the modern world.

Automated Machine Learning (AutoML) is one of the hottest topics in data science today, but what does it mean? 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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