Data Science Workflow
The discussion kicks off with an exploration of the steps data scientists take to create and deploy models. Emphasis is placed on understanding the maturity of the machine learning process and the importance of automation within this workflow. Listeners are encouraged to grasp the foundational concepts that underpin the complexities of the data science pipeline.In this clip
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MLOps Coffee Sessions #10 Analyzing the Article “Continuous Delivery and Automation Pipelines in Machine Learning" // Part 2
Related Questions
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 Streamlining Production Pipelines 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?
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?