Model Deployment Strategies
Many data scientists start by deploying models from notebooks due to their experimental and visual nature. As models mature and transition to production, a git-based workflow becomes essential, allowing for efficient code and data management. This evolution highlights the importance of adapting deployment strategies as models progress through various stages of development.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
699: The Modern Data Stack — with Harry Glaser
Related Questions
What are the ways to deploy AI models as discussed in the episode Analyzing the Google Paper on Continuous Delivery in ML // Part 4 // MLOps Coffee Sessions #17 and the clip Continuous Delivery Insights, as well as in the episode MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3 and the clip Manual ML Processes?
What are the ways to deploy AI models as discussed in the episode MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3 and the clip Manual ML Processes?
How does version control help in machine learning, as discussed in the episode Learn to Code and the clip Data Management Essentials of the podcast 699: The Modern Data Stack — with Harry Glaser?