Discover how to leverage serverless architecture for machine learning pipelines in this informative 38-minute conference talk from the Toronto Machine Learning Series. Explore the benefits and challenges of using serverless approaches for deep learning deployment, training, and operationalization within companies. Learn to utilize services such as Amazon SageMaker, AWS Batch, AWS Fargate, AWS Lambda, and AWS Step Functions to create scalable, affordable, and reliable deep learning workflows. Gain insights into overcoming CPU, GPU, and RAM limitations when organizing model training and inference. Ideal for machine learning engineers and data scientists seeking to optimize their ML and DL deployment strategies.
Overview
Syllabus
Serverless for Machine Learning pipelines.
Taught by
Toronto Machine Learning Series (TMLS)
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