MLOps and DevOps
MLOps parallels DevOps in its support of data science and machine learning model development, showcasing the evolution of essential tools like Docker, Jenkins, and Kubernetes. The rapid development of these technologies, primarily between 2012 and 2015, highlights the nascent state of practices in the field. Understanding the historical context of these tools is crucial for grasping the challenges and advancements in deploying machine learning products at scale.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
SDS 599: MLOps: Machine Learning Operations — with @Miki_ML
Related Questions
When should we consider team adoption of new processes or tools in the context of the episode MLOps Coffee Sessions #13 How to Choose the Right Machine Learning Tool: A Conversation // Jose Navarro and Mariya Davydova and the clip Tooling Decisions in MLOps?
What is the process of training a machine learning model as discussed in the episode Analyzing the Google Paper on Continuous Delivery in ML // Part 4 // MLOps Coffee Sessions #17 and the clip Orchestrated ML Pipeline, as well as in the episode MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3 and the clip Monitoring Model Performance?