Whether you call it “ML Ops”, “Production ML”, “Operationalization” or “Going to Production”, the hard work of building ML-powered data products is thinking beyond the notebook or the model to building repeatable, governed, secure, auditable, and explainable pipelines all the way across the stack. Today’s enterprises realize they need a software 2.0 platform that allows a small team to do all of the above with speed and scale.
In this session, you’ll see live demos and get hands-on with the latest features of Amazon SageMaker, including Jumpstart, Data Wrangler, Feature Store, Pipelines, Model Registry, and Projects.
Amazon SageMaker is enabling a near future where AI is expected and not exceptional for enterprises of all shapes and sizes.
Who should attend
Data scientists, ML engineers, data engineers, MLOps engineers
- 10:00AM – 10:15AM Introduction, Log-in and Logistics
- 10:15AM – 11:00AM Deep-Dive Demo: Jumpstart, Data Wrangler, Feature Store, Projects
- 11:00AM – 11:15PM Coffee break
- 11:15AM – 12:00AM Deep-Dive Demo: Clarify, Pipelines, Model Registry, Inference
- 12:00AM – 12:15AM Live Q&A
- 12:15PM – 13:00PM Break for Lunch
- 13:00PM – 16:00PM Guided, hands-on practice with SageMaker (work at your own pace)
Davide Gallitelli is a Machine Learning Specialist Solutions Architect at AWS. Based in Brussels, he works with customers across Europe to solve business challenges using AI and ML.
Daniël Heres is a Machine Learning Engineer at GoDataDriven. He has experience building data-driven solutions at companies such as bol.com. As a Machine Learning Engineer, he bridges the gap between data engineers and data scientists. Daniël holds an MSc in Computer Science and enjoys creating solutions that bring value out of data using scalable and maintainable software