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Customized model monitoring for near real-time batch inference with Amazon SageMaker

AWS Machine Learning Blog

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. You can use this framework as a starting point to monitor your custom metrics or handle other unique requirements for model quality monitoring in your AI/ML applications.

ML 114
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Experience the new and improved Amazon SageMaker Studio

AWS Machine Learning Blog

This updated user experience (UX) provides data scientists, data engineers, and ML engineers more choice on where to build and train their ML models within SageMaker Studio. Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. She has a decade of experience in DevOps, infrastructure, and ML.

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How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning Blog

Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

DevOps 115
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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 1

AWS Machine Learning Blog

In this series, we walk you through the process of architecting and building an integrated end-to-end MLOps pipeline for a computer vision use case at the edge using SageMaker, AWS IoT Greengrass, and the AWS Cloud Development Kit (AWS CDK). The following diagram illustrates what this could look like for our computer vision pipeline.

DevOps 122
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Accelerate development of ML workflows with Amazon Q Developer in Amazon SageMaker Studio

AWS Machine Learning Blog

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

ML 89
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Top 5 Generative AI Integration Companies to drive Customer Support in 2023

Chatbots Life

10Clouds is a software consultancy, development, ML, and design house based in Warsaw, Poland. Services : Mobile app development, web development, blockchain technology implementation, 360′ design services, DevOps, OpenAI integrations, machine learning, and MLOps. Elite Service Delivery partner of NVIDIA.

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Automate fine-tuning of Llama 3.x models with the new visual designer for Amazon SageMaker Pipelines

AWS Machine Learning Blog

Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. About the Authors Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. Brock Wade is a Software Engineer for Amazon SageMaker.