MLOps practices help data scientists and IT operations professionals collaborate and manage the production machine learning (ML) workflow, including data preparation and building, training, deploying, and monitoring models. During this session, explore the breadth of features in Amazon SageMaker that help you increase automation and improve the quality of your end-to-end workflows.
Learn more about re:Invent 2020 at bit.ly/3c4NSdY
Subscribe:
More AWS videos bit.ly/2O3zS75
More AWS events videos bit.ly/316g9t4
#AWS #AWSEvents
- AWS re:Invent 2020: Implementing MLOps practices with Amazon SageMaker ( Download)
- AWS re:Invent 2021 - Implementing MLOps practices with Amazon SageMaker, featuring Vanguard ( Download)
- AWS AMER Summit May 2021 | Implement MLOps practices with Amazon SageMaker ( Download)
- Implementing MLOps Practices on AWS using Amazon SageMaker ( Download)
- AWS AMER Summit Aug 2021: Implement MLOps practices with Amazon SageMaker ( Download)
- Implementing MLOps practices with Amazon SageMaker ( Download)
- AWS re:Invent 2020: Scaling MLOps on Kubernetes with Amazon SageMaker Operators ( Download)
- Implementing MLOps best practices with Amazon SageMaker - Gili Nachum, AWS ( Download)
- Implement MLOps Practices with Amazon SageMaker Pipelines (Hebrew) ( Download)
- Introducing Amazon SageMaker Pipelines - AWS re:Invent 2020 ( Download)
- AWS re:Invent 2020: MLOps for edge devices with Amazon SageMaker Edge Manager ( Download)
- Implementing MLOps practices with Amazon Sagemaker - Coding Kate Tutorials ( Download)
- Automate MLOps with SageMaker Projects | Amazon Web Services ( Download)
- AWS re:Invent 2020: Productionizing R workloads using Amazon SageMaker, featuring Siemens ( Download)
- AWS re:Invent 2020: How to create fully automated ML workflows with Amazon SageMaker Pipelines ( Download)
