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MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases // Amritha Arun Babu & Abhik Choudhury // #221

Mar 29, 2024
01:00:17
Snipd AI
Guests Amritha Arun Babu Mysore and Abhik Choudhury discuss best practices for building ML Ops architectures, challenges in ML model lifecycle, customer-centric approach, data processing hurdles, diverse paths in MLOps transition, and challenges in transitioning to data science concepts.
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Quick takeaways

  • Data engineers must prioritize scalability, outliers, and compliance in MLOps landscape.
  • Data scientists need to ensure visibility from experimentation to deployment for successful model implementation.

Deep dives

Data Engineering Perspective in Maturity Levels of MLOps

Data engineers focus on data acquisition, transformation, and scalability in the MLOps landscape. Understanding data science terminologies and requirements is crucial to support data scientists effectively. Addressing scalability challenges, handling outliers, and ensuring compliance are key aspects data engineers should prioritize.

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