Production machine learning demand stitching together many tools ranging from open source standards to cloud-specific and third party solutions. This session surveys the current ML deployment technology landscape to contextualize which tools solve for which features off production ML systems such as CI/CD, REST endpoint, and monitoring. It'll help answer the questions: what tools are out there? Where do I start with the MLops tech stack for my application? What are the pros and cons of open source versus managed solutions? This talk takes a features driven approach to tool selection for MLops tacks to provide best practices in the most rapidly evolving field of data science.

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