Streamlining ML Deployment
Zak and Tim discuss the challenges of deploying machine learning models into production, emphasizing the importance of maintaining consistency in transformations from training to inference. They delve into the risks of handoffs between teams and the benefits of using auto ML pipelines to ensure operational success.In this clip
From this podcast
Machine Learning Street Talk (MLST)
WelcomeAIOverlords (Zak Jost)
Related Questions
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?
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?