Artificial intelligence (AI) and machine learning (ML) technologies are at the forefront of reinvention in many products, processes, organizations, and industries. And when it comes to enterprise-grade ML, it’s not just about the model, but how that model is woven into the fabric of an organization.
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.