Anthropic’s Post

I always believed a smaller, faster and laser focused models are the way to go to meet the costing, latency and sustainable inferencing… here we go one of the top model provider Anthropic joining hands with Amazon Web Services (AWS) make this a reality. Model distillation is a paradigm shift in a way how you look at models today. #aws #awsreinvent2024 #awspartners #amazonbedrock #anthropic #claude

Using Amazon Bedrock Model Distillation, you can transfer knowledge, for a specific use-case, from a teacher model to a smaller, faster, cost-efficient student model. Distilled models are up to 500% faster and up to 75% less expensive than original models, with less than 2% accuracy loss for use cases like RAG !

Interesting how now the focus is on performance and cost optimization. We have switched from LLama to GPT when they came out with the new API token price.

Claude 3 Haiku Model Now Available on Amazon Bedrock Introduction Amazon Web Services (AWS) has made a significant announcement with the general availability of fine-tuning for Anthropic’s Claude 3 Haiku model in Amazon Bedrock, specifically in the US West (Oregon) region. This development is noteworthy as Amazon Bedrock stands out as the singular, fully managed service that allows businesses to fine-tune Claude models. This capability provides the opportunity to enhance the Claude 3 Haiku model’s performance by customizing it with your own task-specific training dataset, thereby improving model accuracy, quality, and consistency tailored to your business needs. Understanding Fine-Tuning Fine-tuning is a process that involves customizing a pre-trained large […] https://lnkd.in/dvqgkPPB https://lnkd.in/dnA3hBnU

🚀 TL;DR: Cohere Reranking and Amazon Reranking are now live on Amazon Web Services (AWS) #Bedrock! It’s serverless, pay-as-you-go and has a built-in integration with Knowledge Bases. 🔍 Details: Vector similarity search is able to find a set of relevant documents within a population of 100s of millions. It is generally less costly and fast. With a large enough number of search results, similarity search has high recall but low precision i.e. it is able to find most of the relevant content but it also finds a lot of irrelevant items. LLMs on the other hand have high precision and high recall. For that feat they cost much more and take much longer. Rerankers provide a middle-ground. These models have much higher precision than vector similarity search algorithms but are much faster and less costly when compared to LLMs. Typically, rerankers are used in conjunction with vector search to filter coarse-grained vector search results to the set of most relevant results. This reduces cost and latency of downstream LLM calls while improving accuracy by filtering out noise. 🎉 Now, customers can use the 🆕 Rerank API to call either the Amazon or Cohere Reranking models. Like other Bedrock models, you don’t need to worry about hosting the model - we handle that part. It’s also pay-as-you-go so you only pay for what you use. Customers using our Retrieve or RetrieveAndGenerate APIs can use rerankers for retrieval and RAG by just passing an additional configuration. Release Update: https://lnkd.in/gtJ9rfe7 Blog: https://lnkd.in/gnAZGQUr Cohere Announcement: https://lnkd.in/gnTFyykt #genai #bedrock #aws #cohere #generativeai #bedrockknowledgebases #amazon #reranking #rag

🚀 Exciting news from AWS! Amazon Bedrock Custom Model Import is now generally available! This powerful feature allows you to bring your customized models into Amazon Bedrock seamlessly, alongside existing foundation models, all accessible through a single API. Imagine leveraging models like Meta Llama, Mistral Mixtral, or even your own proprietary architectures without the hassle of managing infrastructure—Bedrock’s serverless setup has you covered! What I love about this update is the flexibility: you can use your existing investments in model tuning, import custom weights from Amazon S3, and take advantage of Bedrock’s native tools like Knowledge Bases and Guardrails. It’s a game-changer for accelerating AI development while If you’re diving into generative AI, this is definitely something to explore! 💡 #AWS #AmazonBedrock #GenerativeAI #TechNews #AI #MachineLearning #Innovation

Hugging Face has just enabled over 100,000 models on AWS Inferentia2 with Amazon SageMaker. You can use Hugging Face to deploy those models on Inferentia2 instances so that you serve inference at scale with all of Sagemaker's managed features for MLOps and governance. Or you can easily experiment with those models by 1-click deployments to Hugging Face Inference Endpoints which just introduced support for Inferentia 2. Inf2 instances are purpose built for deep learning (DL) inference. They deliver high performance at the lowest cost in Amazon EC2 for generative artificial intelligence (AI) models, including large language models (LLMs) and vision transformers. Hugging Face is making it easier for AWS customers to leverage those benefits! Exciting times.

https://lnkd.in/dEy9WaVG One of the critical requirements from our customers is the ability to manage bursts and spiky traffic patterns across a variety of generative AI workloads and disparate request shapes. Some of the key features of cross-region inference include: - Utilize capacity from multiple AWS regions allowing generative AI workloads to scale with demand. - Compatibility with existing Amazon Bedrock API - No additional routing or data transfer cost and you pay the same price per token for models as in your source/primary region. - Become more resilient to any traffic bursts. This means, users can focus on their core workloads and writing logic for their applications powered by Amazon Bedrock. - Ability to choose from a range of pre-configured AWS region sets tailored to your needs.

What's new at AWS 📢 🔰 #AmazonBedrock now supports customers to allocate and track on-demand foundation model usage. 🔰 With this, customers can categorize their GenAI inference costs by department, team, or application using #AWS_cost_allocation_tags. ⚠️ What is Amazon Bedrock: ✔️ It is a fully managed service that offers a choice of high-performing foundation models from leading AI companies via a single API. ✔️ It also provides a broad set of capabilities such as security, privacy, and responsible AI capabilities built in. 🔰 These capabilities help customer to build tailored applications for multiple use cases across different industries. 🔰 Importantly it is helping organizations by ensuring customer trust and data governance. 🔰 You can leverage this feature by creating an application inference profile and tagging it. ⚠️ What is Inference profiles: ✔️ These profiles are a resource in Amazon Bedrock that define a model and one or more Regions ✔️ Inference profile can route model invocation requests. ⚠️ Types of inference profiles: 1️⃣ Cross region inference profiles 2️⃣ Application inference profiles ⚠️ When to use inference profiles: ✔️ Track usage metrics ✔️ Use tags to monitor costs ✔️ Cross-region inference 📌 Explore more about cross-region inference profiles: https://lnkd.in/d_5_JgqJ #aws #awscloud #tagging #costallocation #bedrock #inferenceprofiles #learning #whatsnew #cloudarchitect

Amazon Bedrock Knowledge Bases announces support for cross-region inference, an optional feature that enables developers to seamlessly manage traffic bursts by utilizing compute across different AWS Regions. By using cross-region inference, Amazon Bedrock Knowledge Base customers using RetrieveAndGenerate API will be able to get higher throughput limits and enhanced resilience during periods of peak demand. This offers developers using on-demand inference mode, a seamless solution for managing optimal availability, performance, and resiliency while managing incoming traffic spikes of applications powered by Amazon Bedrock.By opting in, developers no longer have to spend time and effort predicting demand fluctuations. Instead, cross-region inference dynamically routes traffic across multiple regions, ensuring optimal availability for each request and smoother performance during high-usage periods. To learn more about the feature and how to get started, refer to the Amazon Bedrock documentation or this blog below. https://lnkd.in/eq24srME