Remove Machine Learning Remove Scalability Remove Serverless
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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning - AI

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. Organizations typically can’t predict their call patterns, so the solution relies on AWS serverless services to scale during busy times.

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. The full code of the demo is available in the GitHub repository.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

API Gateway is serverless and hence automatically scales with traffic. The advantage of using Application Load Balancer is that it can seamlessly route the request to virtually any managed, serverless or self-hosted component and can also scale well. It’s serverless so you don’t have to manage the infrastructure.

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Integrating Key Vault Secrets with Azure Synapse Analytics

Apiumhub

Also combines data integration with machine learning. Serverless SQL Pools for On-Demand Querying Synapse includes serverless SQL pools for ad-hoc querying of data stored in Azure Data Lake without requiring dedicated compute resources. This is designed for large-scale data storage, query optimization, and analytics.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

As successful proof-of-concepts transition into production, organizations are increasingly in need of enterprise scalable solutions. Private network policies for Amazon OpenSearch Serverless For companies building RAG applications, it’s critical that the data remains secure and the network traffic does not go to public internet.

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Building a Scalable ML Pipeline and API in AWS

Dzone - DevOps

With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. This scalability allows for more frequent and comprehensive reviews.