Davide Gallitelli

​Agentic applications interpret inputs and take actions within a customer’s enterprise environment, and model customization enables agents that understand the business and provides responses or actions that are appropriate for the use case.

Perfect for both newcomers and experienced practitioners looking to accelerate their AI agent development journey, this sessions will teach you to build and deploy AI agents using Amazon SageMaker Studio and Amazon Bedrock AgentCore.

Join us to learn a practical and cost-effective way to customize AI models using spectrum-aware fine-tuning and fully managed training experience provided by Amazon SageMaker training jobs.

While organizations are striving to solve increasingly complex business problems with ML, doing so can require deep technical skills. Amazon SageMaker Canvas is a no-code visual interface that empowers analysts to prepare data, build, and deploy highly accurate ML models.

Machine learning (ML) can solve business problems that help organizations achieve better outcomes. But, how do you take ML beyond technical users to implement it throughout your organization?

Amazon SageMaker Canvas helps business analysts build their own models without writing a single line of code. You can build ML models for common use cases such as regression and forecasting.

Efficient machine learning (ML) models enable desired business outcomes across organizations. In this workshop, learn a no-code approach to ML with Amazon SageMaker Canvas.