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Jeroen Overschie & Jetze Schuurmans - Are you ready for MLOps? 🫵 | PyData London 2024
Discover how MLOps connects development and operations, enabling collaboration, automation, and continuous delivery for machine learning teams and improving efficiency with reproducibility and transparency.
- MLOps connects the worlds of development and operations, leveraging tools like DORA metrics, model drift, and experiment tracking.
- MLOps is not just about technology, but also about people and process.
- Bringing development and operations together requires collaboration, automation, and continuous delivery.
- MLOps promises to bring long-awaited feedback, reduce development cycles, and increase productivity.
- A strong DevOps foundation is essential for MLOps.
- Automation, collaboration, and continuous delivery are key principles of MLOps.
- MLOps best practices include version control, configuration management, containerization, and orchestration.
- Experiment tracking is a crucial component of MLOps, enabling reproducibility and transparency.
- Data drift and model drift must be monitored and addressed to ensure model performance.
- Feedback loops are essential for improving machine learning models and understanding customer needs.
- MLOps is not a replacement for DevOps, but a superset that incorporates MLOps-specific components.
- The state of MLOps is currently on the decline from the peak of inflated expectations.
- A strong foundation in DevOps is necessary for successful MLOps adoption.
- MLOps promises to reduce the gap between development and operations, and improve the efficiency of machine learning teams.
- MLOps is not a one-time implementation, but a continuous process that requires ongoing improvement and optimization.