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![Dataiku O'Reilly: Introducing MLOps]()
O'Reilly: Introducing MLOps
How to scale machine learning in the enterpriseMLOps isn’t just for data scientists; a diverse group of experts across the organization have a role to play not only in the machine learning model lifecycle, but the MLOps strategy as well. Explore a preview version of "Introducing MLOps" before its official release, including the first two chapters.
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- Practical concepts to help data scientists and application engineers operationalise ML models to drive real business change
- Fulfill data science value by reducing friction throughout ML pipelines and workflows
- Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy
- Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable
