CI/CD in ML
Continuous integration and deployment are crucial for ensuring that machine learning models function seamlessly within production environments. Every code change triggers a series of tests to verify core functionalities, preventing disruptions. The integration of model deployments into existing CI/CD processes is particularly valued by mature tech companies, enhancing reliability and efficiency.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?