Training Your PyTorch Model Using Components and Pipelines in Azure ML
By Beatriz Stollnitz, Principal Cloud Advocate at Microsoft In this post, we’ll explore how you can take your PyTorch model training to the next level, using Azure ML. In particular, we’ll see how you can split your training code into multiple steps that can be easily... Read more
Getting Started with ML.NET
Article by Jasmine Greenaway and Carlotta Castelluccio of Microsoft. Machine learning (ML) is everywhere. We use ML-empowered applications every day: when choosing the next TV series to watch based on Netflix recommendations for example, or when asking Alexa to play our favorite song. Soon every application... Read more
Training One Million Machine Learning Models in Record Time with Ray
This blog focuses on scaling many model training. While much of the buzz is around large model training, in recent years, more and more companies have found themselves needing to train and deploy many smaller machine learning models, often hundreds or thousands. Our team has worked with... Read more
Training and Deploying Your PyTorch Model in the Cloud with Azure ML
By Beatriz Stollnitz, Principal Cloud Advocate at Microsoft You’ve been training your PyTorch models on your machine, and getting by just fine. Why would you want to train and deploy them in the cloud? Training in the cloud will allow you to handle larger ML models... Read more
Things Data Scientists Should Know About Productionizing Machine Learning
It is often too much to ask for the data scientist to become a domain expert. However, in all cases the data scientist must develop strong domain empathy to help define and solve the right problems. –  Nina Zumel and John Mount, Practical Data Science with... Read more
Enabling Resilient Machine Learning Systems
Article by Francesca Lazzeri and Bea Stollnitz of Microsoft. Resilient machine learning systems are fast, accurate, and flexible. They assist you in your day-to-day tasks for maximum efficiency, they leverage the latest software and hardware for the fastest performance, and they guide you through complex tasks... Read more
How to Train a Classification Model with TensorFlow in 10 Minutes
Deep learning is everywhere. From sales forecasting to segmenting skin diseases on image data — there’s nothing deep learning algorithms can’t do, given quality data. If deep learning and TensorFlow are new to you, you’re in the right place. This article will show you the entire process of... Read more
Interactive Pipeline and Composite Estimators for Your End-to-End ML Model
A data science model development pipeline involves various components including data injection, data preprocessing, feature engineering, feature scaling, and modeling. A data scientist needs to write the learning and inference code for all the components. The code structure sometimes becomes messier and difficult to interpret for... Read more
MLOps V2 Solution Accelerator – Unifying MLOps at Microsoft
Article by Scott Donohoo and Setu Chokshi of Microsoft. MLOps means different things to different people, however, the fundamental essence of MLOps is to deliver models into productions faster with a consistent, repeatable, and reliable approach. Machine Learning Operations (MLOps) is key to accelerating how data... Read more
Area Under the Curve and Beyond with Integrated Discrimination Improvement and Net Reclassification
TLDR AUC is a good starting metric when comparing the performance of two models but it does not always tell the whole story NRI looks at the new models ability to correctly reclassify cancers and benigns and should be used alongside AUC IDI quantifies improvement of the slopes of... Read more