Data Preparation for Machine Learning at Scale

Presented by

Waqas Dhillon and Jeff Healey, Vertica

About this talk

Machine learning and data science can deliver valuable insights from massive amounts of data. However, before the complex machine learning algorithms can be trained for prediction, you need to address the data exploration and preparation steps. Distributed analytical databases like Vertica can be help you address each step of the machine learning process with a combination of analytical capabilities and computational power. Join this Webcast and see how Vertica’s in-database advanced analytics and machine learning functions help you tackle data preparation and overcome the growing challenge of applying machine learning at scale.

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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.