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Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure dataplatforms in this diagram are neither exhaustive nor prescriptive.
Tools range from dataplatforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. Monitoring Monitor model performance for datadrift and model degradation, often using automated monitoring tools.
Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the dataplatform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the ML dataplatform team, how do you do that? Datadrift.
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