Remove Auto-complete Remove DevOps Remove Generative AI
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Application modernization overview

IBM Journey to AI blog

Application modernization is the process of updating legacy applications leveraging modern technologies, enhancing performance and making it adaptable to evolving business speeds by infusing cloud native principles like DevOps, Infrastructure-as-code (IAC) and so on. Let us explore the Generative AI possibilities across these lifecycle areas.

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Enabling generative AI self-service using Amazon Lex, Amazon Bedrock, and ServiceNow

AWS Machine Learning Blog

Application Auto Scaling is enabled on AWS Lambda to automatically scale Lambda according to user interactions. The solution will confer with responsible AI policies and Guardrails for Amazon Bedrock will enforce organizational responsible AI policies.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

By surrounding unparalleled human expertise with proven technology, data and AI tools, Octus unlocks powerful truths that fuel decisive action across financial markets. Visit octus.com to learn how we deliver rigorously verified intelligence at speed and create a complete picture for professionals across the entire credit lifecycle.

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Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

AWS Machine Learning Blog

If you prefer to generate post call recording summaries with Amazon Bedrock rather than Amazon SageMaker, checkout this Bedrock sample solution. They are designed for real-time, interactive, and low-latency workloads and provide auto scaling to manage load fluctuations. The format of the recordings must be either.mp4,mp3, or.wav.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process.

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Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC

AWS Machine Learning Blog

Launch the instance using Neuron DLAMI Complete the following steps: On the Amazon EC2 console, choose your desired AWS Region and choose Launch Instance. You can update your Auto Scaling groups to use new AMI IDs without needing to create new launch templates or new versions of launch templates each time an AMI ID changes.

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Falcon 2 11B is now available on Amazon SageMaker JumpStart

AWS Machine Learning Blog

It’s a next generation model in the Falcon family—a more efficient and accessible large language model (LLM) that is trained on a 5.5 It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. After deployment is complete, you will see that an endpoint is created.

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