#SDBigData Meetup #29

AutoML is a trending feature of Machine Learning that is making it easier for any of us to more easily and successively create good data science models. Come join your favorite San Diego group of interested individuals to learn more.

Food and drink will be graciously provided by our executive meetup sponsor, Qubole.

Agenda as follows:
5:30-6:00pm Socializing over food and beverages
6:00-6:15pm Welcome and announcements from Trace3
6:15-7:15pm Automated Machine Learning with Qubole
7:15-7:30pm Wrap-up and extended discussions

More about what we'll be learning about:
Automated Machine Learning (AutoML) is one of the hottest topics in data science today, but what does it mean? In this workshop, Danny D. Leybzon (a seasoned data scientist and Solutions Architect at Qubole) will give 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. Throughout the presentation, Danny will leverage Apache Spark (a framework popular with data scientists who need to scale their machine learning workloads to Big Data) and Apache Zeppelin notebooks, as well as popular Python libraries such as Pandas, Plotly, and bayes-opt. Participants will walk away from this workshop with in depth knowledge of hyperparameter tuning (using grid search, random search, Bayesian optimization, and genetic algorithms) and will have been exposed to new tools for automating their machine learning workflows.

About our sponsor, Qubole:
Qubole delivers an autonomous, Self-Service Platform for Big Data Analytics built on Amazon Web Services, Microsoft and Google Clouds. Qubole was started by the team that built and ran Facebook's Data Service when they founded and authored Apache Hive. With Qubole, a data scientist can now spin up hundreds of clusters on their public cloud of choice and begin creating ad hoc and/or batch queries in 3 minutes. Qubole is used by many leading firms for end-to-end data processing, and takes away the burdens of scalability and administration.

About our speaker, Danny:
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.