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MLOps for GenAI Applications // Harcharan Kabbay // #256

MLOps.community

NOTE

Importance of Observability in Machine Learning Operations

Observability is crucial in machine learning (ML) operations, transcending beyond traditional logging and monitoring. It emphasizes the importance of actively tracking model performance and data integrity to ensure ongoing accuracy and reliability. When replacing manual processes with ML models, it's vital to recognize that the inherited knowledge may change over time, making observability key to identifying data drift and performance dips before they lead to issues. Monitoring metrics like runtime, processed rows, and accuracy during training can facilitate early detection of anomalies. Additionally, understanding the broader data pipeline context is essential, as issues may arise from data changes before they reflect in model performance. With advancements in large language models (LLMs), the complexity of metrics expands, necessitating evaluations at various stages, including the quality of retrieved results. Enhanced visualization tools and proactive alerting systems can aid in maintaining model accuracy and operational integrity in an evolving data landscape.

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