Remove APIs Remove Benchmark Remove Workshop
article thumbnail

Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning

Workshops – In these hands-on learning opportunities, in 2 hours, you’ll be able to build a solution to a problem, and understand the inner workings of the resulting infrastructure and cross-service interaction. Builders’ sessions – These highly interactive 60-minute mini-workshops are conducted in small groups of fewer than 10 attendees.

APIs 101
article thumbnail

Transition your Amazon Forecast usage to Amazon SageMaker Canvas

AWS Machine Learning

You can also either use the SageMaker Canvas UI, which provides a visual interface for building and deploying models without needing to write any code or have any ML expertise, or use its automated machine learning (AutoML) APIs for programmatic interactions.

APIs 114
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

AWS Machine Learning

Together, these AI-driven tools and technologies aren’t just reshaping how brands perform marketing tasks; they’re setting new benchmarks for what’s possible in customer engagement. From our experience, artifact server has some limitations, such as limits on artifact size (because of sending it using REST API).

APIs 120
article thumbnail

Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

In addition, they use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide an answer to the user’s request. In Part 1, we focus on creating accurate and reliable agents.

article thumbnail

Train gigantic models with near-linear scaling using sharded data parallelism on Amazon SageMaker

AWS Machine Learning

To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script. You can follow the comments in the script and API document to learn more about where SMP APIs are used. Benchmarking performance. We benchmarked sharded data parallelism in the SMP library on both 16 and 32 p4d.24xlarge

Scripts 88
article thumbnail

New performance improvements in Amazon SageMaker model parallel library

AWS Machine Learning

Finally, we’ll benchmark performance of 13B, 50B, and 100B parameter auto-regressive models and wrap up with future work. For training a different model type, you can follow the API document to learn about how to apply SMP APIs. Benchmarking performance. Finally, we benchmark SMP with both of the latest features enabled.

article thumbnail

A review of purpose-built accelerators for financial services

AWS Machine Learning

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. The CUDA API and SDK were first released by NVIDIA in 2007. GPU PBAs, 4% other PBAs, 4% FPGA, and 0.5%

Benchmark 106