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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. Choose the us-east-1 AWS Region from the top right corner. Choose Manage model access.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning - AI

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. However, managing the complex infrastructure required for big data workloads has traditionally been a significant challenge, often requiring specialized expertise.

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Integrating Key Vault Secrets with Azure Synapse Analytics

Apiumhub

This opens a web-based development environment where you can create and manage your Synapse resources, including data integration pipelines, SQL queries, Spark jobs, and more. Link External Data Sources: Connect your workspace to external data sources like Azure Blob Storage, Azure SQL Database, and more to enhance data integration.

Azure 91
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Top 8 predictive analytics tools compared

CIO

Visual IDE for data pipelines; RPA for rote tasks. AWS SageMaker. Full integration with AWS, third-party marketplace, serverless options. Driverless AI offers automated pipeline; AI adapts to incoming data. AWS SageMaker. Turn-key AWS instances begin at 99 cents per hour. Free trial. Free tier.

Analytics 206
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Core technologies and tools for AI, big data, and cloud computing

O'Reilly Media - Ideas

Many companies are just beginning to address the interplay between their suite of AI, big data, and cloud technologies. I’ll also highlight some interesting uses cases and applications of data, analytics, and machine learning. Data Platforms. Data Integration and Data Pipelines. Model lifecycle management.

Big Data 107
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Boost team productivity with Amazon Q Business Insights

AWS Machine Learning - AI

They are available at no additional charge in AWS Regions where the Amazon Q Business service is offered. Log groups prefixed with /aws/vendedlogs/ will be created automatically. Choose Enable logging to start streaming conversation and feedback data to your logging destination. For more information, see Policy evaluation logic.