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How to Turn Your AI Idea Into a Scalable Product: A Technical Guide
Go Here to Read this Fast! How to Turn Your AI Idea Into a Scalable Product: A Technical Guide
Tag: AI
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Accelerated PyTorch inference with torch.compile on AWS Graviton processors
Originally PyTorch used an eager mode where each PyTorch operation that forms the model is run independently as soon as it’s reached. PyTorch 2.0 introduced torch.compile to speed up PyTorch code over the default eager mode. In contrast to eager mode, the torch.compile pre-compiles the entire model into a single graph in a manner that’s optimal for […]Originally appeared here:
Accelerated PyTorch inference with torch.compile on AWS Graviton processors -
Access control for vector stores using metadata filtering with Knowledge Bases for Amazon Bedrock
In November 2023, we announced Knowledge Bases for Amazon Bedrock as generally available. Knowledge bases allow Amazon Bedrock users to unlock the full potential of Retrieval Augmented Generation (RAG) by seamlessly integrating their company data into the language model’s generation process. This feature allows organizations to harness the power of large language models (LLMs) while […]Originally appeared here:
Access control for vector stores using metadata filtering with Knowledge Bases for Amazon Bedrock -
Accenture creates a custom memory-persistent conversational user experience using Amazon Q Business
Traditionally, finding relevant information from documents has been a time-consuming and often frustrating process. Manually sifting through pages upon pages of text, searching for specific details, and synthesizing the information into coherent summaries can be a daunting task. This inefficiency not only hinders productivity but also increases the risk of overlooking critical insights buried within […]Originally appeared here:
Accenture creates a custom memory-persistent conversational user experience using Amazon Q Business -
Create an end-to-end serverless digital assistant for semantic search with Amazon Bedrock
With the rise of generative artificial intelligence (AI), an increasing number of organizations use digital assistants to have their end-users ask domain-specific questions, using Retrieval Augmented Generation (RAG) over their enterprise data sources. As organizations transition from proofs of concept to production workloads, they establish objectives to run and scale their workloads with minimal operational […]Originally appeared here:
Create an end-to-end serverless digital assistant for semantic search with Amazon Bedrock -
Using a JSON Agent with LangChain, LangSmith and OpenAI’s GPT-4o
Developing a chatbot to answer questions about a JSON dataset
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Using a JSON Agent with LangChain, LangSmith and OpenAI’s GPT-4oGo Here to Read this Fast! Using a JSON Agent with LangChain, LangSmith and OpenAI’s GPT-4o
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Build a self-service digital assistant using Amazon Lex and Knowledge Bases for Amazon Bedrock
Organizations strive to implement efficient, scalable, cost-effective, and automated customer support solutions without compromising the customer experience. Generative artificial intelligence (AI)-powered chatbots play a crucial role in delivering human-like interactions by providing responses from a knowledge base without the involvement of live agents. These chatbots can be efficiently utilized for handling generic inquiries, freeing up […]Originally appeared here:
Build a self-service digital assistant using Amazon Lex and Knowledge Bases for Amazon Bedrock -
Identify idle endpoints in Amazon SageMaker
Amazon SageMaker is a machine learning (ML) platform designed to simplify the process of building, training, deploying, and managing ML models at scale. With a comprehensive suite of tools and services, SageMaker offers developers and data scientists the resources they need to accelerate the development and deployment of ML solutions. In today’s fast-paced technological landscape, […]Originally appeared here:
Identify idle endpoints in Amazon SageMakerGo Here to Read this Fast! Identify idle endpoints in Amazon SageMaker
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Indian language RAG with Cohere multilingual embeddings and Anthropic Claude 3 on Amazon Bedrock
Media and entertainment companies serve multilingual audiences with a wide range of content catering to diverse audience segments. These enterprises have access to massive amounts of data collected over their many years of operations. Much of this data is unstructured text and images. Conventional approaches to analyzing unstructured data for generating new content rely on […]Originally appeared here:
Indian language RAG with Cohere multilingual embeddings and Anthropic Claude 3 on Amazon Bedrock
