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Explore the intricacies of building machine learning infrastructure in this 43-minute video from the Inside TensorFlow series. Join Software Engineer Mingsheng Hong as he delves into research and engineering challenges in ML infrastructure development. Discover the similarities between big data and ML infrastructure, understand the importance of investing in ML infrastructure, and examine a case study on building a new TensorFlow runtime. Learn about ML programs as computational graphs, vectorized normalization, and Eager execution. Compare ML infrastructure to SQL query processing, explore input pipelines, and understand graph processing workflows. Dive into topics such as graph rewrites, cost models, data statistics, constraint propagation, and storage/access optimizations. Gain insights into distributed and parallel execution, and recognize how ML infrastructure relates to data infrastructure with unique twists. Access additional resources through provided links and consider potential collaborations in this field.
