Broadcast Date: June 24, 2022
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.
- 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
- Michael Hsieh - Sr. AI/ML Specialist SA
- Shelbee Eigenbrode - Principla AI/ML Specialist SA
To learn more about the services featured in this talk, please visit:
Service How To
December 19th, 2018 | 1:00 PM PT
Developing Deep Learning Models for Computer Vision with
Amazon EC2 P3 Instances.