Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007)

Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007) Benefits Upon successful completion of this course, students will master essential skills to:  Make data available in Azure Machine Learning.  Work with compute targets in Azure Machine Learning.  Run a training script as a command job in Azure Machine Learning.  Track model training with MLflow in jobs.  Register an MLflow model in Azure Machine Learning.  Deploy a model to a managed online endpoint.  Training Prerequisites To maximize the benefits of this course, participants should have familiarity with the data science process. While the course doesn't delve deeply into data science concepts, a basic understanding is recommended. Additionally, familiarity with Python is essential, as the course focuses on utilizing the Python SDK for interacting with Azure Machine Learning. Azure Machine Learning DP-3007 training course Outline Module 1: Make Data Available in Azure Machine Learning  Introduction  Understand URIs  Create a datastore  Create a data asset  Exercise: Make data available in Azure Machine Learning  Module 2: Work with Compute Targets in Azure Machine Learning  Introduction  Choose the appropriate compute target  Create and use a compute instance  Create and use a compute cluster  Exercise: Work with compute resources  Module 3: Work with Environments in Azure Machine Learning  Introduction  Understand environments  Explore and use curated environments  Create and use custom environments  Exercise: Work with environments  Module 4: Run a Training Script as a Command Job in Azure Machine Learning  Introduction  Convert a notebook to a script  Run a script as a command job  Use parameters in a command job  Exercise: Run a training script as a command job  Module 5: Track Model Training with MLflow in Jobs  Introduction  Track metrics with MLflow  View metrics and evaluate models  Exercise: Use MLflow to track training jobs  Module 6: Register an MLflow Model in Azure Machine Learning  Introduction  Log models with MLflow  Understand the MLflow model format  Register an MLflow model  Exercise: Log and register models with MLflow  Module 7: Deploy a Model to a Managed Online Endpoint  Introduction  Explore managed online endpoints  Deploy your MLflow model to a managed online endpoint  Deploy a model to a managed online endpoint  Test managed online endpoints  Exercise: Deploy an MLflow model to an online endpoint