Introducing Meta Llama 3 Models on Azure AI Model Catalog
Unveiling the next generation of Meta Llama models on Azure AI: Meta Llama 3 is here! With new capabilities, including improved reasoning and Azure AI Studio integrations, Microsoft and Meta are pushing the frontiers of innovation. Dive into enhanced contextual understanding, tokenizer efficiency and a diverse model ecosystem—ready for you to build and deploy generative AI models and applications across your organization. Explore Meta Llama 3 now through Azure AI Models as a Service and Azure AI Model Catalog, where next generation models scale with Azure's trusted, sustainable and AI-optimized high-performance infrastructure.An Enterprise Design for Azure Machine Learning - An Architect's Viewpoint
This article provides an opinionated design for an enterprise-level data science capability, implemented within an Azure data platform. The guidance provides a starting point for the design of an ML platform that fits your business requirements.Fundamental of Deploying Large Language Model Inference
"Deploying Large Language Models: Tips & Tricks" explores the complexities of hosting large language models, including challenges such as model size, sharding, and computational resources. The blog offers insights into the technical expertise, infrastructure setup, and significant investment required. It delves into the intricacies of model serving, inference workflows, and the careful planning needed to manage the high volume of requests and data. The post provides valuable tips and tricks for effectively navigating these challenges, making it essential reading for anyone interested in understanding the intricacies of hosting large language models and the associated costs.
Potential Use Cases for Generative AI
Azure’s generative AI, with its Copilot and Custom Copilot modes, offers a transformative approach to various industries, including manufacturing, retail, public sector, and finance. Its ability to automate repetitive tasks, enhance creativity, and solve complex problems optimizes efficiency and productivity. The potential use cases of Azure’s generative AI are vast and continually evolving, demonstrating its versatility and power in addressing industry-specific challenges and enhancing operational efficiency. As more organizations adopt this technology, the future of these sectors looks promising, with increased productivity, improved customer experiences, and innovative solutions. The rise of Azure’s generative AI signifies a new era of intelligent applications that can generate content, insights, and solutions from data, revolutionizing the way industries operate and grow.
WebNN: Bringing AI Inference to the Browser
Unlock the Future of AI with WebNN: Bringing Machine Learning to Your Browser Discover how the groundbreaking Web Neural Network API (WebNN) is revolutionizing web development by enabling powerful machine learning computations directly in your browser. From real-time AI interactions to privacy-preserving data processing, WebNN opens up a world of possibilities for creating intelligent, responsive web applications. Dive into our comprehensive guide to understand the architecture, see code examples, and explore exciting use cases that showcase the true potential of WebNN. Whether you're a seasoned developer or just curious about the future of web-based AI, this article is your gateway to the cutting-edge of technology. Read on to find out more!Predict steel quality with Azure AutoML in manufacturing
This post will guide you through how we, Lotta Åhag and Gustav Kruse, used Azure AutoML and the 'Enterprise Scale ML (ESML) solution accelerator for Azure', to build an end-2-end machine learning solution in 6 weeks. The value of the solution is estimated to reduce 3.35 tons of Co2 emissions of propane and decrease electricity usage of 90MWh per year after putting the solution into production. The ecological impact is much higher less quality rejections will save a lot of resources including coal (and gas) needed to produce the steel. We collaborated with the Epiroc Data Scientist, Erik Rosendahl, who worked with the ESML templates for operation and governance. This is the story we want to share, about an end-2-end, machine learning solution on Azure. We wanted to leverage AI for steel manufacturing, in the area of heat treatment quality, with the goal to enhance the process to be able to reduce CO2. We got help from 2 student data scientists who wanted to execute this as their master thesis. In 6 weeks, they managed to leverage Azure Machine Learning and the ESML AI factory at Epiroc, using AutoML to build a machine learning model w an end-2-end pipeline from datalake to Power BI report. The team quickly got its own set of Azure resources as an ESML Project which ensured both enterprise grade scale and security – and out came a new AI innovation, with ecological wins - 3.35 ton C02 reduction and 30% less quality rejections. //Jonas Jern, Head of Digital Innovation, Epiroc Drilling Tools ABA Guide to Optimizing Performance and Saving Cost of your Machine Learning (ML) Service - Part 2
Now that you've basic idea of what your ML model service will look like, let's look at some recommendations on Azure and specifically Azure Machine Learning We go in depth into how to select the right Azure SKU for running your ML Service and various Azure ML settings and limits.
