MLOps as a Team - Raphaël Hoogvliets
DataTalks.Club
Optimizing MLOps Practices
This chapter focuses on best practices for organizing machine learning projects, highlighting the importance of separating exploratory code from production elements while ensuring accessibility. It discusses the significance of data versioning and team composition in implementing MLOps, as well as the necessity of understanding organizational challenges for effective deployment. Additional topics include managing dependencies, evaluating MLOps readiness, and the trade-offs between containerization strategies to enhance workflow efficiency.
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