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Learn how to accelerate the transition from Jupyter notebooks to production-ready machine learning systems in this 14-minute conference talk recorded at the AI Agent Builders Summit in London. Discover practical strategies, tools, and methodologies that streamline the deployment pipeline, reduce development time, and bridge the gap between experimental data science work and scalable production environments. Explore best practices for code refactoring, containerization, CI/CD implementation, and infrastructure considerations that enable faster time-to-market for ML models. Gain insights into common bottlenecks in the notebook-to-production workflow and learn proven techniques to overcome them, making your machine learning projects more efficient and deployment-ready.
