Jan. 31, 2022, 5 p.m. | Matt Mayo Editor

KDnuggets www.kdnuggets.com

Given how uncertain ML projects are, this is an incremental strategy that you can adopt as your project matures; it includes test examples to provide a clear idea of how these tests look in practice, and a complete project implementation is available on GitHub. By the end of the post, you’ll be able to develop more robust ML pipelines.

2022 jan tutorials learning machine machine learning overviews testing

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Software Engineer, Data Tools - Full Stack

@ DoorDash | Pune, India

Senior Data Analyst

@ Artsy | New York City