Book Image

Azure Machine Learning Engineering

By : Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz
Book Image

Azure Machine Learning Engineering

By: Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz

Overview of this book

Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You’ll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide. Throughout the book, you’ll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You’ll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework. By the end of this Azure Machine Learning book, you’ll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.
Table of Contents (17 chapters)
1
Part 1: Training and Tuning Models with the Azure Machine Learning Service
7
Part 2: Deploying and Explaining Models in AMLS
12
Part 3: Productionizing Your Workload with MLOps

Labeling image data using the Data Labeling feature of Azure Machine Learning

In the field of computer vision, object detection is a challenging task for predicting the location of objects in an image and predicting the object types. Just like any other supervised machine learning task, in order to train an object detection model, we need to have training data and, in this case, a lot of labeled data, as deep learning works best on a large labeled dataset. Data scientists who develop computer vision models know how tedious and time-consuming it can be to label images, and even more time-consuming when it comes to labeling images for object detection models. The Azure Machine Learning (Azure ML) service has a powerful feature called Data Labeling, which significantly enhances the user experience for image labeling, leveraging built-in capabilities such as ML-assisted labeling by automatically training a model to pre-label images for you to review, accelerating the labeling process.

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