State-of-the-art models are rapidly increasing in size and complexity. These models can be difficult to train because of cost, time, and skill sets required to optimize memory and compute. In this session, learn how Amazon SageMaker enables customers to train large models by using clusters of accelerated compute instances and software libraries to partition models and optimize communication between instances. Learn concepts and techniques such as pipeline parallelism, tensor parallelism, optimizer state sharding, activation checkpointing, and others. Discuss best practices and tips and pitfalls in configuring training for these state-of-the-art large models.
Join this session to learn how to prepare data for ML in minutes using Amazon SageMaker. SageMaker offers t...
AWS AI/ML Solutions?
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In this session, explore how AWS services can help you move from idea to production with ML and an end-to-end data strategy.
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As part of this session, Public Broadcasting Service (PBS) shares their personalization story and its impact.
With Amazon Kendra, you can build an intelligent search solution, powered by ML, to find accurate answers from the unstructured content in your enterprise.
In this session, learn how automation and AI services from AWS can help your customer service and media teams reclaim up to 95 percent of their time spent doing manual moderation.
Join this session to learn how to make the shift toward more automation and proactive mechanisms with ML-powered insights that can help your developer teams innovate faster.
Amazon SageMaker Canvas is a visual, point-and-click service that makes it easy for business analysts to build ML models and generate accurate predictions without writing code or having ML expertise.
Join this session to learn how to prepare data for ML in minutes using Amazon SageMaker. SageMaker offers tools to simplify data preparation so that you can label, prepare, and understand your data.
Amazon SageMaker provides all the tools and libraries you need to build ML models.
High-performance and cost-effective techniques, including real-time, asynchronous, and batch, are needed to scale model deployments to maximize your ML investments.
MLOps practices help data scientists & IT operations professionals collaborate & manage the production ML workflow, including data preparation & building and training, deploying, & monitoring models
In this session, explore how to choose the proper instance for ML training and inference based on model size, complexity, and performance requirements.
In this session, learn how to use Deep Learning Containers to build your custom ML environment and how to implement model training and inference with Deep Learning Containers in Amazon SageMaker.
In this chalk talk, dive into building computer vision (CV) applications at the edge for predictive maintenance, industrial IoT, and more.
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning.