Course Overview
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.
Requirements
- Creating cloud resources in Microsoft Azure.
- Using Python to explore and visualize data.
- Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
- Working with containers To gain these prerequisite skills, take the following free online training before attending the course:
- Explore Microsoft cloud concepts.
- Create machine learning models.
- Administer containers in AzureIf you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
Target audiences
- 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.
Curriculum
- 1 Section
- 19 Lessons
- 4 Days
Expand all sectionsCollapse all sections
- Topics19
- 1.1Design a machine learning solution Design a data ingestion strategy for machine learning projects Introduction Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution Exercise: Design a data ingestion strategy Knowledge check
- 1.2Design a machine learning model training solution Introduction Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options Exercise: Design a model training strategy Knowledge check
- 1.3Design a model deployment solution Introduction Understand how model will be consumed Decide on real-time or batch deployment Exercise – Design a deployment solution
- 1.4Design a machine learning operations solution Introduction Explore an MLOps architecture Design for monitoring Design for retraining Knowledge check
- 1.5Explore and configure the Azure Machine Learning workspace Explore Azure Machine Learning workspace resources and assets Introduction Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace Exercise – Explore the workspace Knowledge check
- 1.6Explore developer tools for workspace interaction Introduction Explore the studio Explore the Python SDK Explore the CLI Exercise-Explore the developer tools Knowledge check
- 1.7Make data available in Azure Machine Learning Introduction Understand URIs Create a datastore Create a data asset Exercise – Make data available Knowledge check
- 1.8Work 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 Knowledge check
- 1.9Work with environments in Azure Machine Learning Introduction Understand environments Explore and use curated environments Create and use custom environments Exercise – Work with environments Knowledge check
- 1.10Experiment with Azure Machine Learning Find the best classification model with Automated Machine Learning Introduction Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models Exercise – Find the best classification model Knowledge check
- 1.11Track model training in Jupyter notebooks with MLflow Introduction Configure MLflow for model tracking in notebooks Train and track models in notebooks Exercise – Track model training Knowledge check
- 1.12Optimize model training with Azure Machine Learning 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 Knowledge check
- 1.13Track model training with MLflow in jobs Introduction Track metrics with MLflow View metrics and evaluate models Exercise – Use MLflow to track training jobs Knowledge check
- 1.14Perform hyperparameter tuning with Azure Machine Learning Introduction Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning Exercise – Run a sweep job Knowledge check
- 1.15Run pipelines in Azure Machine Learning Introduction Create components Create a pipeline Run a pipeline job Exercise – Run a pipeline job Knowledge check
- 1.16Manage and review models in Azure Machine Learning 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 Knowledge check
- 1.17Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Introduction Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard Exercise – Explore the Responsible AI dashboard Knowledge check
- 1.18Deploy and consume models with Azure Machine Learning 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 Knowledge check
- 1.19Deploy a model to a batch endpoint Introduction Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints Exercise – Deploy an MLflow model to a batch endpoint Knowledge check
Instructor
