MLOps and Software Practices
The discussion highlights the differences between traditional software development practices and the emerging field of MLOps. Key insights reveal that machine learning models are as critical as code and require a unique approach to CI/CD pipelines. As organizations seek to establish effective patterns in this evolving landscape, the importance of reproducibility and transparency in the development process becomes paramount.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Machine Learning as a Software Engineering Discipline with Dillon Erb - #404
Related Questions
What is the process of training a machine learning model as discussed in the episode MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3 and the clip Monitoring Model Performance?
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?