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Discover an Enterprise Managed Platform for efficient AI/ML solution operationalization at scale in this 34-minute conference talk. Learn about the platform's automated AI/ML lifecycle stages, offering Model as a Service (MaaS), Data as a Service (DaaS), and Function as a Service (FaaS). Explore how it establishes engineering standards and best practices while providing managed care for applications, models, data, and feature pipelines. Understand the platform's use of Databricks for compute, orchestration, and model management, as well as Databricks MLflow for tracking and model registry. Gain insights into how models are exposed as REST APIs using Azure Kubernetes and Azure APIM for API governance. Examine the integration of Datadog and PagerDuty for monitoring and alerting. Delve into the platform's configuration management and helper methods for registering artifacts and models, as well as capturing logs for evaluation and retraining. The talk is presented by Vikas Vennavali, ML Engineer Lead at Starbucks Coffee Company, and provides additional resources for further exploration in MLOps and LLM.
