Machine learning (ML) can be a complex process for any size company. The lack of integration between workflow steps and tools not only makes it difficult, but time-consuming. That’s why startups use Amazon SageMaker to build, train, and deploy ML models. We'll dive deep into demonstrating SageMaker’s advanced features that help you train and iterate on your ML models faster. You'll learn techniques to transform your ML research project into a production-ready service.
34:01Don’t let the idea of integrating artificial intelligence (AI) or machine learning (ML) into your workflow intimidate you.
26:18Watch this session to review AWS AV solutions for the toolchain including data ingest, management, labeling, simulation, and model training.
47:42To help learn from those who’ve done it before, we’ve gathered AI/ML founders from some of the world’s top startups to give a peek behind the scenes into the secrets of their own success.
16:07Discover how to set up a data lake and implement it into an ML experiment workflow, how to prepare an end-to-end workflow to easily share the workload, and other tips for scaling your startup.
36:56Learn how to maximize resource utilization to find performance bottlenecks, and how to reduce overall training and inference costs.