Kieran Kavanagh

Many organizations have started to optimize their ML workload deployments using MLOps mechanisms. Learn how this works in the real world and how Amazon enabled the automation and reusability of ML solutions across a number of different business workloads—from optimizing product search results on Amazon.com to running its transportation network and ensuring faster delivery times.

Amazon.com launched in 1995 with a modest architecture and a grand vision to be Earth’s most customer-centric company. Learn how Amazon.com got big fast, and see their evolution from a monolithic architecture to a massively scalable system based on service-oriented architecture and microservices.

Machine learning involves more than just training models; you need to source and prepare data, engineer features, select algorithms, train and tune models, and then deploy those models and monitor their performance in production. Learn how Amazon Consumer Payments uses Amazon SageMaker, AWS Glue, AWS Step Functions, Amazon API Gateway, AWS Lambda, AWS Batch, Amazon Elastic Container Registry...

Machine learning involves more than just training models; you need to source and prepare data, engineer features, select algorithms, train and tune models, and then deploy those models and monitor their performance in production. Learn how Amazon Consumer Payments uses Amazon SageMaker, AWS Glue, AWS Step Functions, Amazon API Gateway, AWS Lambda, AWS Batch, Amazon Elastic Container Registry...

Machine learning involves more than just training models; you need to source and prepare data, engineer features, select algorithms, train and tune models, and then deploy those models and monitor their performance in production. Learn how to set up an ETL pipeline to analyze customer data using Amazon SageMaker, AWS Glue, and AWS Step Functions.