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Falcon 2 11B is now available on Amazon SageMaker JumpStart

AWS Machine Learning Blog

It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. Discover Falcon 2 11B in SageMaker JumpStart You can access the FMs through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. We recommend using SageMaker Studio for straightforward deployment and inference.

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Top MLOps Tools Guide: Weights & Biases, Comet and More

Unite.AI

It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets. BentoML : BentoML is a Python-first tool for deploying and maintaining machine learning models in production. What is MLOps?

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Top 25 AI Tools for Software Development in 2025

Marktechpost

TabNine TabNine is an AI-powered code auto-completion tool developed by Codota, designed to enhance coding efficiency across a variety of Integrated Development Environments (IDEs). Kite Kite is an AI-driven coding assistant specifically designed to accelerate development in Python and JavaScript.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

Right now, most deep learning frameworks are built for Python, but this neglects the large number of Java developers and developers who have existing Java code bases they want to integrate the increasingly powerful capabilities of deep learning into. For this reason, many DJL users also use it for inference only.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on.

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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning Blog

This post details how Purina used Amazon Rekognition Custom Labels , AWS Step Functions , and other AWS Services to create an ML model that detects the pet breed from an uploaded image and then uses the prediction to auto-populate the pet attributes. About the Authors Mason Cahill is a Senior DevOps Consultant with AWS Professional Services.

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

AWS Machine Learning Blog

It manages the availability and scalability of the Kubernetes control plane, and it provides compute node auto scaling and lifecycle management support to help you run highly available container applications. His work spans multilingual text-to-speech, time series classification, ed-tech, and practical applications of deep learning.