Discover how to master Azure Machine Learning to optimize your artificial intelligence projects. Our course “Train and deploy a machine learning model with Azure Machine Learning (DP-3007)” guides you through the crucial steps from data preparation and compute resource configuration to training and deploying models for real-time predictions.
Whether you want to create and manage custom environments, monitor model performance with MLflow, or deploy your solutions effectively, this learning path is designed to equip professionals with the necessary skills to excel in the dynamic field of machine learning. Join our course ‘Train and deploy a machine learning model with Azure Machine Learning (DP-3007)’ to turn your knowledge into concrete actions and drive your AI projects to new heights of efficiency and innovation.”
Module 1 : Make data available in Azure Machine Learning
Module 2 : Work with compute targets in Azure Machine Learning
Module 3 : Work with environments in Azure Machine Learning
Module 4 : Run a training script as a command job in Azure Machine Learning
Module 5 : Track model training with MLflow in jobs
Module 6 : Register an MLflow model in Azure Machine Learning
Module 7 : Deploy a model to a managed online endpoint
In a world where data is the new oil, knowing how to organize, store, and make available your precious data in Azure Machine Learning is essential. Our training guides you through the steps to understand URIs, create an efficient datastore, and manage your data assets optimally.
Choosing the right compute target can make all the difference in executing your machine learning projects. Learn how to select the right compute target, create and use a compute instance, and a compute cluster to maximize the efficiency of your machine learning tasks.
Understanding and navigating the various environments of Azure Machine Learning is crucial for the success of your projects. Our training covers everything from predefined environments to creating and using your own custom environments, thus speeding up your development.
Transforming a notebook into a script and running it as a command job may seem complex, but our training makes this process transparent. You will also learn how to integrate parameters into your command jobs, thus optimizing your training scripts.
Using MLflow to track the metrics of your models and evaluate their performance is an invaluable skill. Our course shows you how to make the most of MLflow to keep track of your training and make informed decisions based on reliable data.
Discover how to register your MLflow models effectively in Azure Machine Learning, understand the MLflow model format, and ensure that your models are production-ready.
Deploying your models to managed online endpoints is the final step in putting your work into production. Our training provides you with the tools and knowledge needed to successfully deploy your MLflow models and test online endpoints, ensuring that your models perform well and are ready to use.
Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00
ITTA
Route des jeunes 35
1227 Carouge, Suisse
Monday to Friday, from 8:30 am to 06:00 pm.
