Remove Auto-complete Remove DevOps Remove LLM
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

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. The use of multiple external cloud providers complicated DevOps, support, and budgeting. Follow Octus on LinkedIn and X.

DevOps 90
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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. Ease of integration of APIs with channel front-end layers.

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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

The Hugging Face containers host a large language model (LLM) from the Hugging Face Hub. They are designed for real-time, interactive, and low-latency workloads and provide auto scaling to manage load fluctuations. You can use other languages such as Spanish, French, or Portuguese, but the quality of the completions may degrade.

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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.

Python 112
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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. LLM training configurations. Is it fast and reliable enough for your workflow?

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Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

AWS Machine Learning Blog

The NVIDIA NeMo Framework provides a comprehensive set of tools, scripts, and recipes to support each stage of the LLM journey, from data preparation to training and deployment. Training Now that our data preparation is complete, we’re ready to train our model with the created dataset.

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Amazon Bedrock Marketplace now includes NVIDIA models: Introducing NVIDIA Nemotron-4 NIM microservices

AWS Machine Learning Blog

Scalable infrastructure – Bedrock Marketplace offers configurable scalability through managed endpoints, allowing organizations to select their desired number of instances, choose appropriate instance types, define custom auto scaling policies that dynamically adjust to workload demands, and optimize costs while maintaining performance.