Remove Benchmark Remove Best practices Remove Engineering
article thumbnail

Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock

AWS Machine Learning

In this post, we share comprehensive best practices and scientific insights for fine-tuning Meta Llama 3.2 Our recommendations are based on extensive experiments using public benchmark datasets across various vision-language tasks, including visual question answering, image captioning, and chart interpretation and understanding.

article thumbnail

LLM-as-a-judge on Amazon Bedrock Model Evaluation

AWS Machine Learning

Curated judge models : Amazon Bedrock provides pre-selected, high-quality evaluation models with optimized prompt engineering for accurate assessments. Expert analysis : Data scientists or machine learning engineers analyze the generated reports to derive actionable insights and make informed decisions. 0]}-{evaluator_model.split('.')[0]}-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}"

Metrics 108
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

Elevate customer experience by using the Amazon Q Business custom plugin for New Relic AI

AWS Machine Learning

The challenge: Resolving application problems before they impact customers New Relic’s 2024 Observability Forecast highlights three key operational challenges: Tool and context switching – Engineers use multiple monitoring tools, support desks, and documentation systems. The following diagram illustrates the workflow.

article thumbnail

Best practices to build generative AI applications on AWS

AWS Machine Learning

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

article thumbnail

Cohere Embed multimodal embeddings model is now available on Amazon SageMaker JumpStart

AWS Machine Learning

All text-to-image benchmarks are evaluated using Recall@5 ; text-to-text benchmarks are evaluated using NDCG@10. Text-to-text benchmark accuracy is based on BEIR, a dataset focused on out-of-domain retrievals (14 datasets). Generic text-to-image benchmark accuracy is based on Flickr and CoCo. This example uses ml.g5.xlarge,

Benchmark 110
article thumbnail

Best practices for Amazon SageMaker Training Managed Warm Pools

AWS Machine Learning

In this post, we outline the key benefits and pain points addressed by SageMaker Training Managed Warm Pools, as well as benchmarks and best practices. Benchmarks. We performed benchmarking tests to measure job startup latency using a 1.34 Best practices for using warm pools. Data Input Mode.

article thumbnail

Maximizing ROI with CPQ: 10 Best Practices for Sales Success

Cincom

This article outlines 10 CPQ best practices to help optimize your performance, eliminate inefficiencies, and maximize ROI. Automate Price Calculations and Adjustments Utilize real-time pricing engines within CPQ to dynamically calculate prices based on market trends, cost fluctuations, and competitor benchmarks.