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As the discipline of machine learning operations (MLOps) matures, it’s becoming clear that, in practice, building and deploying ML models pose additional challenges compared to the traditional software development lifecycle. The development and deployment of ML models require certain features such as reproducibility, recoverability, maintainability, audibility, scalability, and compute management, among others. Using Flyte, a data- and machine-learning-aware open source orchestration tool, we’ll see how to address these challenges and abstract them out to provide a broader understanding of how to surmount them. In this talk, Samhita Alla: ● Presents the challenges in the context of ML model development and deployment ● Introduces Flyte and dive into the ways through which it provides solutions ● Elaborates on the ML-specific features offered by Flyte ● Does a best practices walk-through to develop models fast and productionize ML code effectively