Machine Learning Street Talk (MLST) Jordan Edwards: ML Engineering and DevOps on AzureML
Jun 3, 2020
Jordan Edwards, Principal Program Manager for AzureML at Microsoft, dives into the world of ML DevOps and the challenges of deploying machine learning models. He discusses how to bridge the gap between science and engineering, emphasizing model governance and testing. Jordan shares insights from the recent Microsoft Build conference, highlighting innovations like FairLearn and GPT-3. He also introduces his maturity model for ML DevOps and explores the complexities of collaboration in machine learning workflows, making for a thought-provoking conversation.
AI Snips
Chapters
Books
Transcript
Episode notes
Jordan's Background
- Jordan Edwards started at Microsoft working on distributed systems for Bing.
- He transitioned to engineering systems, introducing CI/CD to ML-first organizations.
Data Scientists vs. Software Engineers
- Data scientists often have research backgrounds and aren't classically trained as software engineers.
- They are trained to publish papers, not write production-ready code, requiring different tools.
ML Requires a Team
- Production enterprise ML requires diverse expertise, including data engineers, data scientists, and other specialists.
- No single person can handle all aspects of this complex process.
