Please complete your modules and practice assessment as soon as possible. Vouchers are limited.
Please complete your modules and practice assessment as soon as possible. Vouchers are limited.
Manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning and MLflow.
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
Take the practice exam to test your knowledge and confirm your readiness for your certification. Passing the practice assessment with 70%+ is required before you can claim your free YES x Microsoft certification voucher.
You must complete every module in the learning path and pass the practice exam to qualify for the free certification voucher. Vouchers are limited so hurry and complete your course today!
As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets.
Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).
Learn about how to connect to data from the Azure Machine Learning workspace. You’re introduced to datastores and data assets.
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
Learn how to find the best classification model with automated machine learning (AutoML). You’ll use the Python SDK (v2) to configure and run an AutoML job.
Learn how to use MLflow for model tracking when experimenting in notebooks.
Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.
Learn how to track model training with MLflow in jobs when running scripts.
Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.
Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.
Learn how to log and register an MLflow model in Azure Machine Learning.
Explore model explanations, error analysis, counterfactuals, and causal analysis by creating a Responsible AI dashboard. You’ll create and run the pipeline in Azure Machine Learning using the Python SDK v2 to generate the dashboard.
Learn how to deploy models to a managed online endpoint for real-time inferencing.
Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you trigger a batch scoring job.
Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involves identifying the services you’ll use and creating an optimal working environment for your development team.
Choose the various language models that are available through the Microsoft Foundry’s model catalog. Understand how to select, deploy, and test a model, and to improve its performance.
Use the Microsoft Foundry SDK to develop AI applications with Microsoft Foundry projects.
Learn about how to use prompt flow to develop applications that leverage language models in the Microsoft Foundry.
Retrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to ground prompts with your data. Microsoft Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions.
Train a base language model on a chat-completion task. The model catalog in Microsoft Foundry offers many open-source models that can be fine-tuned for your specific model behavior needs.
Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.
Evaluating copilots is essential to ensure your generative AI applications meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your generative AI applications using the tools and features available in the Azure AI Studio.
Take the practice exam to test your knowledge and confirm your readiness for certification. Passing the practice assessment with 70%+ is required before you can claim your free YES x Microsoft certification voucher.
If you have completed all required modules and passed your practice assessment, you qualify for a free certification voucher. Submit your voucher claim form to move forward with your final exam.