Explore distributed deep learning techniques and reliable MLOps practices at Uber in this 30-minute conference talk by Travis Addair. Dive into the early adoption of Horovod, understand distributed deep learning concepts, and compare parameter servers with the Allreduce technique. Examine benchmarking results, learn about deep learning applications in research and production environments, and discover feature stores for efficient model training. Investigate preprocessing techniques, Spark ML pipelines, and Petastorm for data access in deep learning. Address challenges of training on large datasets, explore Spark 3.0's resource-aware scheduling, and learn about Horovod Lambda for CPU-based data processing. Gain insights into online prediction using Neuropod, workflow authoring, and the process of ideating, defining, evaluating, and deploying deep learning models within a single script. Conclude with an overview of feature engineering, model construction, deployment, and Elastic Horovod's control flow capabilities. Read more