Who should take this course?
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.
Prerequisites
Microsoft Azure AI Fundamentals (AI-900) is recommended, or the equivalent experience.
- 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
The Training Covers These Topics:
1 – Design a data ingestion strategy for machine learning projects
- Identify your data source and format
- Choose how to serve data to machine learning workflows
- Design a data ingestion solution
2 – Design a machine learning model training solution
- Identify machine learning tasks
- Choose a service to train a machine learning model
- Decide between compute options
3 – Design a model deployment solution
- Understand how model will be consumed
- Decide on real-time or batch deployment
4 – Explore Azure Machine Learning workspace resources and assets
- Create an Azure Machine Learning workspace
- Identify Azure Machine Learning resources
- Identify Azure Machine Learning assets
- Train models in the workspace
5 – Explore developer tools for workspace interaction
- Explore the studio
- Explore the Python SDK
- Explore the CLI
6 – Make data available in Azure Machine Learning
- Understand URIs
- Create a datastore
- Create a data asset
7 – Work with compute targets in Azure Machine Learning
- Create and use a compute instance
- Create and use a compute cluster
8 – Work with environments in Azure Machine Learning
- Understand environments
- Explore and use curated environments
- Create and use custom environments
9 – Find the best classification model with Automated Machine Learning
- Preprocess data and configure featurization
- Run an Automated Machine Learning experiment
- Evaluate and compare models
10 – Track model training in Jupyter notebooks with MLflow
- Configure MLflow for model tracking in notebooks
- Train and track models in notebooks
11 – Run a training script as a command job in Azure Machine Learning
- Convert a notebook to a script
- Run a script as a command job
- Use parameters in a command job
12 – Track model training with MLflow in jobs
- Track metrics with MLflow
- View metrics and evaluate models
13 – Run pipelines in Azure Machine Learning
- Create components
- Create a pipeline
- Run a pipeline job
14 – Perform hyperparameter tuning with Azure Machine Learning
- Define a search space
- Configure a sampling method
- Configure early termination
- Use a sweep job for hyperparameter tuning
15 – Deploy a model to a managed online endpoint
- 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
16 – Deploy a model to a batch endpoint
- 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