ML Data Flows
Jordan discusses designing scalable data flows for ML models, emphasizing the importance of monitoring resource usage. The challenges of adapting frameworks to unique model requirements are highlighted, with Docker containers proposed as a solution for heterogeneous compute pipelines.In this clip
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Machine Learning Street Talk (MLST)
Jordan Edwards: ML Engineering and DevOps on AzureML
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