all AI news
What Does It Really Mean To Do MLOps And What Is The Data Engineer's Role?
April 16, 2022, 9 p.m. | Tobias Macey
Data Engineering Podcast www.dataengineeringpodcast.com
Summary
Putting machine learning models into production and keeping them there requires investing in well-managed systems to manage the full lifecycle of data cleaning, training, deployment and monitoring. This requires a repeatable and evolvable set of processes to keep it functional. The term MLOps has been coined to encapsulate all of these principles and the broader data community is working to establish a set of best practices and useful guidelines for streamlining adoption. In this episode Demetrios Brinkmann and David …
More from www.dataengineeringpodcast.com / Data Engineering Podcast
Making Email Better With AI At Shortwave
4 days, 4 hours ago |
www.dataengineeringpodcast.com
Designing A Non-Relational Database Engine
1 week, 4 days ago |
www.dataengineeringpodcast.com
Reconciling The Data In Your Databases With Datafold
1 month, 1 week ago |
www.dataengineeringpodcast.com
When And How To Conduct An AI Program
1 month, 3 weeks ago |
www.dataengineeringpodcast.com
Jobs in AI, ML, Big Data
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
Vice President, Data Science, Marketplace
@ Xometry | North Bethesda, Maryland, Lexington, KY, Remote
Field Solutions Developer IV, Generative AI, Google Cloud
@ Google | Toronto, ON, Canada; Atlanta, GA, USA