Vertica Machine Learning for the Enterprise – What’s New

Presented by

Jeff Healey, Badr Ouali, and Waqas Dhillon, Vertica

About this talk

Vertica supports the entire data science lifecycle by enabling data scientists to perform machine learning at scale and put their models into production. We have recently added many important features, including AutoML, Auto Data-Prep, stepwise and Bayesian search, statistical tests, dynamic charting, and other graphics. In order to support users to work with other data science tools, we have enhanced our integration capabilities with support for XGBoost, sklearn, and export to Python. Join us to hear about these and many other interesting capabilities that can help you with your machine learning needs.

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The Vertica Unified Analytics Platform is built to handle the most demanding analytic use cases and is trusted by thousands of leading data-driven enterprises around the world, including Etsy, Bank of America, Uber, and more. Based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and built-in machine learning capability, Vertica enables data analytics teams to easily apply these powerful functions to large and demanding analytical workloads. Vertica unites the major public clouds and on-premises data centers, as needed, and integrates data in cloud object storage and HDFS without forcing any data movement. Available as a SaaS option, or as a customer-managed system, Vertica helps teams combine growing data siloes for a more complete view of available data. Vertica features separation of compute and storage, so teams can spin up storage and compute resources as needed, then spin down afterwards to reduce costs.