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

Senior Machine Learning Engineer

@ GPTZero | Toronto, Canada

ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)

@ HelloBetter | Remote

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Senior Applied Data Scientist

@ dunnhumby | London

Principal Data Architect - Azure & Big Data

@ MGM Resorts International | Home Office - US, NV