Evolution of MLOps
Jordan's journey at Microsoft highlights the evolution of MLOps, bridging the gap between data science and software engineering. His focus on integrating tools for data pipelines and model development showcases the complexity of modern machine learning workflows.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Jordan Edwards: ML Engineering and DevOps on AzureML
Related Questions
How will large language models (LLMs) and AI change software engineering and the software development lifecycle (SDLC) as discussed in the episode 787: MLOps: The Job and The Key Tools — with Demetrios Brinkmann and the clip LLM Tools Explained, as well as in the episode Meta’s Joe Spisak on Llama 3.1 405B and the Democratization of Frontier Models | Training Data?
What does a machine learning engineer do?