Evolving MLOps
The discussion highlights the transition from traditional MLOps to LLM Ops and FM Ops, emphasizing the reduced complexity in managing resources, as interactions primarily occur through APIs. Despite the streamlined process, the importance of experimentation remains crucial, particularly in designing and updating prompts to optimize model performance. The foundational lifecycle of data collection, experimentation, deployment, and monitoring persists, showcasing both evolution and continuity in the field.In this clip
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The Gradient
Shreya Shankar: Machine Learning in the Real World
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