Operationalizing Machine Learning for the Enterprise using Python & Vertica

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Presented by

Waqas Dhillon, Badr Ouali and Jeff Healey, Vertica

About this talk

Under the Hood: Data science has the potential to help businesses gain a competitive edge, but the difficulty of putting machine learning models into production is a huge impediment. Vertica ML unifies machine learning with data warehousing, which not only democratizes access to core machine learning capabilities but also greatly simplifies putting these models into production. In this session, we will share with you how you can use Vertica’s Python interface to perform the entire machine learning cycle – from data preparation to model deployment on very large datasets spanning several nodes in a cluster. We will also demonstrate the use Vertica as a repository for your machine learning models so you can archive, manage, and deploy these models on your enterprise data whether on-premises or in the cloud.

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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, Intuit, 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.