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Implementing MLOps practices with Amazon SageMaker

MLOps practices help data scientists and IT operations professionals collaborate and manage the production ML workflow, including data preparation and building and training, deploying, and monitoring models. During this session, explore the features in Amazon SageMaker Pipelines that help you increase automation, track data lineage, catalog ML models for production, improve the quality of your end-to-end workflows, and support governance. Also, learn how to use SageMaker projects, which provide MLOps templates for incorporating CI/CD practices into your ML pipelines.

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Achieve high-performance and cost-effective model deployment
Achieve high-performance and cost-effective model deployment

High-performance and cost-effective techniques, including real-time, asynchronous, and batch, are needed to...

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Streamline content moderation workflows with AI/ML
Streamline content moderation workflows with AI/ML

In this session, learn how automation and AI services from AWS can help your customer service and media tea...

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Opt-in to Future Emails
Yes, I'd like Amazon Web Services (AWS) to share the latest news about AWS services and related offerings with me by email, post or telephone.

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