Applications based on machine learning (ML) can provide tremendous business value. One of the advantages of running ML on the AWS Cloud is that you can continually optimize your workloads and reduce your costs. The development, training, maintenance, and performance tuning of ML models is an iterative process that requires continuous improvement. Determining the optimum state in the model while going through the permutations and combinations of model parameters and data dependencies to adjust is just one leg of the journey. There is more to optimizing the cost of ML than just algorithm performance and model tuning. This video explains how to apply such optimization to ML workloads and shares best practices for training and inference.

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☁️ AWS Online Tech Talks cover a wide range of topics and expertise levels through technical deep dives, demos, customer examples, and live Q&A with AWS experts. Builders can choose from bite-sized 15-minute sessions, insightful fireside chats, immersive virtual workshops, interactive office hours, or watch on-demand tech talks at your own pace. Join us to fuel your learning journey with AWS.

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