Harness the power of Large Language Models with Azure Machine Learning prompt flow
Unlock the full potential of your AI solutions with our latest blog on prompt flow! Discover how to effectively assess and refine your prompts and flows, leading to production-ready, impactful LLM-infused applications. Don't miss out on these game-changing insights!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.A Guide to Optimizing Performance and Saving Cost of your Machine Learning (ML) Service - Part 1
This is a multi-part blog series that will teach you how to optimize your machine learning model service and save money. Tuning the performance of your Azure Machine Learning endpoint will help you make better use of your Azure Machine Learning resources, reduce costs, and increase throughput.Azure OpenAI path to production – A case study with PowerBuddy
Join us on a transformative journey as PowerSchool revolutionizes the educational landscape with Azure OpenAI. Discover how generative AI is enhancing every facet of learning, from personalized content creation to sophisticated grading systems. Dive into PowerSchool's innovative approaches to integrating advanced AI models in K-12 education, and explore their strategic monitoring and scaling of AI applications. This is where the future of education takes shape – powered by AI and crafted by PowerSchool.Announcing registries in Azure Machine Learning to operationalize models and pipelines at scale
We are excited to announce the public preview of registries in Azure Machine Learning. Registries in Azure Machine Learning are organization wide repositories of machine learning assets such as models, environments, and components. Registries provide a central platform for cataloging and operationalizing machine learning models across various personas, teams and environments involved in the machine learning lifecycle. Registries foster better collaboration among data science teams by offering a central platform to share and discover machine learning models and pipelines.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 AB
