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7min chapter
DOP 299: Managing Your AI Workloads With KitOps
DevOps Paradox
The Essentials of Experimentation Tracking in AI/ML
This chapter explores the significance of experimentation tracking in AI and machine learning, emphasizing structured experiments and the necessity of uniform datasets for model evaluation. It also discusses current tools like MLflow and the role of OpenTelemetry in facilitating continuous monitoring and standardization in AI/ML production environments.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode