Learn how to streamline and manage the end-to-end machine learning lifecycle using MLOps in this 47-minute video. Explore best practices for initiating projects, experimentation, data engineering, and model operationalization. Discover recommended MLOps processes, organizational models for implementation, and deployment strategies. Gain insights into the differences between cluster and Spark pools. Follow along with hands-on examples and reference architectures to put MLOps into practice effectively. Conclude with a Q&A session to address specific concerns and deepen understanding of MLOps concepts.
Overview
Syllabus
Introduction
Recommended MLOps Process
Best Practices for Initiaing Project
Best Practices for Experimentation
Best Practices for Data Engineering
Best Practices for Model Operationalization
Cluster vs Spark Pools
Organizational Models for MLOps Implementation
Deployment
Conclusion and QnA
Taught by
Data Science Dojo
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