By using website you agree to our use of cookies as described in our cookie policy. Learn More


Checklist Report cover

TDWI Checklist Report | Seven Best Practices for Machine Learning on a Data Lake

March 30, 2018

As organizations collect and analyze increasing amounts of data, they are turning to the data lake as the platform to perform more advanced analytics such as machine learning.

Why a data lake? Machine learning often requires an iterative process that can drain performance on a traditional warehouse. Data lakes are made for scale and experimentation. They also provide ample, diverse training data for the most comprehensive learning experience, which makes algorithmic assessments more accurate and successful when put into production.

This TDWI Checklist Report presents more details about the data requirements for advanced analytics on a data lake. The bulk of the report is about best practices for analytics—with a focus on machine learning—as performed on data lakes.

Your e-mail address is used to communicate with you about your registration, related products and services, and offers from select vendors. Refer to our Privacy Policy for additional information.

TDWI Membership

Get immediate access to training discounts, video library, research, and more.

Find the right level of Membership for you.