Improve governance of your ML projects with Amazon SageMaker

As companies are increasingly adopting ML in mainstream enterprise applications, they require control over and visibility into their ML projects, also known as ML governance. Amazon SageMaker recently launched new capabilities to help customers define user permissions, and create a single source of truth for model information and gain insights into model performance and troubleshoot deviations from a single view. Join us for a deep dive into Amazon SageMaker Role Manager, SageMaker Model Cards, and SageMaker Model Dashboard.

Easily build, train, and deploy ML models using geospatial data

Today, the majority of all data generated contains geospatial information, but only a small fraction of it is used for ML because accessing, processing, and visualizing the data is complex, time consuming, and expensive. With Amazon SageMaker’s new geospatial capabilities, you can access readily available geospatial data sources, easily process large-scale geospatial datasets with purpose-built operations, accelerate model building with pretrained ML models, and use rich visualization tools to explore predictions on an interactive map. Join us for an interactive demo and learn how to make faster and smarter decisions using the power of geospatial data.

Who should attend: Data Scientists, Data Engineers, ML Developers, ML Engineers

Use Amazon SageMaker to build generative AI applications

Tune in to learn how to fine-tune and deploy large language and vision models on Amazon SageMaker to build your generative AI applications. We will explore popular generative AI use cases such as image generation, search, chat, and document summarization. Join our experts for a deep dive conversation and interactive demo.

No-code ML for faster decision making

Organizations everywhere use ML to accurately predict outcomes and make faster business decisions. However, this often requires preparing, building, training, and deploying ML models. With Amazon SageMaker Canvas, non-technical business users can generate accurate ML predictions on their own without requiring any ML experience or writing a single line of code. In this session, you'll learn how you can use SageMaker Canvas to connect, prepare, analyze, and explore data, automatically build ML models, and collaborate with data scientists to increase productivity. You'll even learn how you can import ML models from anywhere and generate predictions directly in Amazon SageMaker Canvas.

Deploying machine learning models for inference

Maximizing inference performance while reducing cost is critical to delivering great customer experiences through ML. Amazon SageMaker provides a breadth and depth of fully managed deployment features to achieve optimal inference performance and cost at scale without the operational burden. In this episode, learn how to use SageMaker inference capabilities to quickly deploy ML models in production for any use case, including hyper-personalization, Generative AI, and Large Language Models (LLMs).

Deliver high-performance ML models faster with MLOps tools

Are you curious how you can use no-code ML to accurately predict outcomes and make faster business decisions? Join us for this special edition of SageMaker Fridays to learn how Amazon SageMaker Canvas enables non-technical users to generate accurate ML predictions on their own without requiring any ML experience or writing a single line of code. You'll even be able to hear from our experts about the most frequently asked no-code ML questions.

Accelerate your ML journey with Amazon SageMaker low-code ML tools

The machine learning (ML) journey requires continuous experimentation and rapid prototyping to be successful. In order to create highly accurate models, data scientists have to first experiment with feature engineering, model selection, and optimization techniques, which can be time-consuming and expensive. In this session, learn how low-code ML tools, including Amazon SageMaker Data Wrangler, Amazon SageMaker Autopilot, and Amazon SageMaker JumpStart, make it easier to experiment faster and bring highly accurate models to production more quickly and efficiently.

Prepare ML data faster and at scale with Amazon SageMaker

Data preparation for ML is a difficult process. It requires extracting and normalizing data and performing feature engineering, which can be time consuming. With Amazon SageMaker you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, bias detection, and visualization from a single visual interface. In this session, you'll learn how you can use Amazon SageMaker to reduce the time it takes to aggregate and prepare structured data for ML from weeks to minutes.

Build high performance, energy-efficient, and cost-effective ML applications with Amazon SageMaker, AWS Trainium, and AWS Inferentia

Managing the underlying infrastructure while building, training, and deploying machine learning (ML) models at scale can be technically intensive without the right tools and expertise. Amazon SageMaker is a fully managed ML service to build, train, and deploy ML models so you can focus on ML innovation instead of tedious infrastructure management. SageMaker offers you a choice of high-performance ML accelerators such as AWS Trainium and AWS Inferentia which are purpose-built for large-scale models such as LLMs and deliver 50% lower cost-to-train and 70% lower cost per inference. In this session, learn how you can build your own generative AI applications using Amazon SageMaker, AWS Trainium, and AWS Inferentia. In addition, we will also share how you can get started by using self-managed services such as AWS Deep Learning Container, AWS Deep learning AMIs, and ML frameworks and model libraries such as TensorFlow, PyTorch and Hugging Face.