Remove APIs Remove Benchmark Remove Presentation
article thumbnail

GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. Based on customer feedback for the experimental APIs we released in GraphStorm 0.2, introduces refactored graph ML pipeline APIs. Specifically, GraphStorm 0.3 In addition, GraphStorm 0.3

APIs 119
article thumbnail

Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStartĀ 

AWS Machine Learning

This post explores these relationships via a comprehensive benchmarking of LLMs available in Amazon SageMaker JumpStart, including Llama 2, Falcon, and Mistral variants. We provide theoretical principles on how accelerator specifications impact LLM benchmarking. Additionally, models are fully sharded on the supported instance.

Benchmark 122
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

Common Challenges in Automated API Testing: Overcoming Obstacles with Expert Solutions

CSM Magazine

Automated API testing stands as a cornerstone in the modern software development cycle, ensuring that applications perform consistently and accurately across diverse systems and technologies. Continuous learning and adaptation are essential, as the landscape of API technology is ever-evolving.

APIs 52
article thumbnail

Learn how Amazon Ads created a generative AI-powered image generation capability using Amazon SageMaker

AWS Machine Learning

Next, we present the solution architecture and process flows for machine learning (ML) model building, deployment, and inferencing. Acting as a model hub, JumpStart provided a large selection of foundation models and the team quickly ran their benchmarks on candidate models. The Amazon API Gateway receives the PUT request (step 1).

article thumbnail

Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. However, hosting models presents its own unique set of challenges. Having a unified developer experience when accessing custom models or base models through Amazon Bedrockā€™s API.

APIs 139
article thumbnail

Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart

AWS Machine Learning

Jina Embeddings v2 is the preferred choice for experienced ML scientists for the following reasons: State-of-the-art performance ā€“ We have shown on various text embedding benchmarks that Jina Embeddings v2 models excel on tasks such as classification, reranking, summarization, and retrieval. The answer should only use the presented context.

Benchmark 117
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 102