Broadcast Date: June 24, 2022
Level: 200
MLOps practices help accelerate and streamline the ML development lifecycle. ML engineers, join us for a hands-on demo showing how to use Amazon SageMaker to implement MLOps practices, including automating ML workflows, building CI/CD pipelines for ML, monitoring models in production, and standardizing model governance.
Learning Objectives
- Explore how to automate ML workflows to accelerate data preparation and model building, training, and experiments
- Learn how to build continuous integration and delivery (CI/CD) pipelines to reduce model management overhead
- Find out how to monitor quality of ML models by automatically detecting bias, model drift, and concept drift
Who Should Attend?
ML engineers, MLOps Engineers, data scientists
Speakers
- Michael Hsieh - Sr. AI/ML Specialist SA
- Shelbee Eigenbrode - Principla AI/ML Specialist SA
Learn More
To learn more about the services featured in this talk, please visit:
https://aws.amazon.com/sagemaker/mlops
Intro body copy here about 2018 re:Invent launches.
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