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MLOps vs ML Orchestration // Ketan Umare // #183

MLOps.community

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Exploring ML Ops Content and Building a Flexible Architecture

This chapter emphasizes the importance of immersing oneself in ML Ops content and highlights the need for engineering containerization, writing QA code, implementing distributed queues, and other tasks to improve performance. The speakers also discuss the complexity of accommodating different use cases and suggest building a flexible architecture, with a subtle recommendation to use Airflow 1.10 instead of version 2.0.

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