Under the Hood: Introduction to Vertica In-database Machine Learning

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Presented by

Waqas Dhillon and Josh Poduska, Vertica

About this talk

Vertica’s in-database machine learning supports the entire predictive analytics process with massively parallel processing and a familiar SQL interface. Vertica has the only native, in-database, distributed machine learning library on the market today; complete with easy-to-use features for blazing fast ingestion, complex data preparation, the fastest distributed model building available, thorough model evaluation, detailed model management, simple model deployment, and advanced integration with popular machine learning and visualization tools. During this demo you’ll get a firsthand look at Vertica machine learning using a public IoT dataset from the Irish Smart Meter project. The demonstration will cover data exploration and visualization, data preparation, model building, model scoring, model evaluation, and model management. Learn how data scientists and analysts can leverage Vertica to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises.

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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.