Training machine learning (ML) models at scale often requires significant investments. In this session, learn how Amazon SageMaker reduces the time and cost to train and tune large-scale ML models without the need to manage infrastructure. Learn how you can take advantage of high-performance compute infrastructure without worrying about scale, train your models faster using distributed training libraries, and arrive at the most accurate predictions using advanced hyperparameter tuning methods.
22:21Developing a data-driven culture requires a mindset shift. Learn from AWS customer examples and Amazon itself about how to address these challenges and transform your data into a strategic asset.
27:37In this session, tailored to business leaders, AWS Enterprise Strategists share how organizations can embrace AI/ML, along with stories on how some customers use them to achieve profound results.
18:27Many organizations are sitting on a treasure trove of data but don’t know how to get value out of it. In this session you will learn how AWS enables leading use cases of putting data to work.
20:42In this session, learn how companies across industries are using artificial intelligence (AI) to address use cases that create measurable results.
24:18Carrier’s CDO Bobby George and AWS Enterprise Strategist John Clark talk about earning buy-in for data transformation, evolving a data-centric culture, and how data strategy relates to sustainability.
28:04In this session, learn how the simple integration of Amazon Personalize into your existing websites and marketing systems can create high-value personalization at every touchpoint.
14:21Many organizations have a fragile or inadequate document processing pipeline. In this session, learn how to take advantage of AWS’s latest innovations in AI and ML to process documents efficiently.
23:26In this session, you will learn about AWS Contact Center Intelligence (CCI) and Conversational AI solutions and how to integrate them into your preferred contact center solution.
22:05In this session, we will share how AI services like Amazon Kendra and Amazon Lex can help you build conversational interfaces into any application quickly, with no machine learning expertise required.
19:23In this session, hear how Amazon Connect unlocks AI/ML-powered innovation that offers personalized digital experiences, improved agent productivity, and captures business insights.
17:21Join this session on predictive maintenance to learn how to prevent downtime in industrial machinery by automatically detecting abnormal behavior and proactively acting on potential failures.
21:09In this session, learn how startup Vale partnered with ClearScale to use modern AI/ML technology to create a new platform for watching movies and TV shows through its Streaming Guide application.
25:27The ML journey requires experimentation. In this session, learn how tools like Amazon SageMaker allow you to experiment faster so you can focus more on refining predictions and less on low-level code.
29:30In this session, explore Amazon SageMaker machine learning operations (MLOps) features and learn how to increase automation and improve the quality of your ML workflows.
27:26Join this session to learn how to increase productivity with SageMaker Studio notebook capabilities, from simplified data preparation to automatic conversion of notebook code to production-ready jobs.
23:25High-performance, cost-effective model deployment is critical to ML return on investment. In this session, learn how to use Amazon SageMaker’s inference abilities to quickly deploy ML models at scale.
27:12Organizations everywhere are using ML to accurately predict outcomes. In this session, you will learn how to use Amazon SageMaker Canvas for cases across sales, marketing, finance, and operations.
16:28In this session, hear how Amazon Omics supports large-scale health and life science analysis and collaborative research with purpose-built data stores, scalable workflows, and multimodal analytics.
