Engineering machine learning data pipelines

Oct 3rd, 2018

Gary Allemann, MD at Master Data Management

Machine learning, artificial intelligence and similar “big data” and advanced analytics capabilities are popular areas of investment, as businesses seek become data driven.

Yet, data scientists spend more time finding and preparing data than actually developing analyses. This series of 15 minute webinars, with Master Data Management partner, Syncsort, will examine the end to end process of engineering machine learning data pipelines

Pulling in Data from Multiple Sources

Monday, October 15th at 10:30am ET
Get a look at a better way to get high performance data access and integration on your production cluster, without spending a bunch of time coding or tuning.
Register Now >

Big Data Quality – Cleansing Data at Scale

Monday, October 22nd at 10:30am ET
Learn how you can feed production machine learning models with shiny clean data while spending zero time on coding and performance tuning.
Register Now >

Finding and Matching Duplicates to Resolve Entities

Monday, October 29th at 10:30am ET
Learn how to tackle the tough, compute intensive problem of finding all instances of data about a person or company to get a complete 360 view, regardless of how massive or how many datasets you’ve combined. And do it without coding or tuning.
Register Now >

Tracking Data Lineage from the Source

Monday, November 5th at 10:30am ET
Getting a coherent source to consumption view of where data came from and how it was changed along the way is way harder than it should be in modern data architectures. Learn the tools to address this problem.
Register Now >

Streaming New Data as It Changes

Monday, November 12th at 10:30am ET
Explore how to set up a continuous streaming flow of data from data sources, so that as the sources change, new data automatically gets pushed through the same transformation and cleansing data flow.
Register Now >