Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Level 2: Contextual Knowledge
Build and Operate Machine Learning Solutions with Azure Machine Learning
Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. Learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions.
This learning path assumes that you have experience of training machine learning models with Python and open-source frameworks like Scikit-Learn, PyTorch, and Tensorflow. If not, you should complete the Create Machine Learning Models learning path before starting this one.
This learning path assumes that you have experience of training machine learning models with Python and open-source frameworks like Scikit-Learn, PyTorch, and Tensorflow. If not, you should complete the Create Machine Learning Models learning path before starting this one.
Courses in this program
1) Introduction to the Azure Machine Learning SDK
Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models.
In this module, you will learn how to:
In this module, you will learn how to:
- Provision an Azure Machine Learning workspace.
- Use tools and interfaces to work with Azure Machine Learning.
- Run code-based experiments in an Azure Machine Learning workspace.
Estimated effort 1 hour
English language
2) Train a Machine Learning Model with Azure Machine Learning
Learn how to use Azure Machine Learning to train a model and register it in a workspace.
In this module, you will learn how to:
In this module, you will learn how to:
- Use a ScriptRunConfig to run a model training script as an Azure Machine Learning experiment.
- Create reusable, parameterized training scripts.
- Register trained models.
3) Work with Data in Azure Machine Learning
Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions.
Learning objectives:
Learning objectives:
- Create and use datastores in an Azure Machine Learning workspace.
- Create and use datasets in an Azure Machine Learning workspace.
4) Work with Compute in Azure Machine Learning
One of the key benefits of the cloud is the ability to use scalable, on-demand compute resources for cost-effective processing of large data. In this module, you'll learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.
Learning objectives:
Learning objectives:
- Work with environments.
- Work with compute targets.
5) Orchestrate Machine Learning with Pipelines
Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning.
Learning objectives:
Learning objectives:
- Create Pipeline steps.
- Pass data between steps.
- Publish and run a pipeline.
- Schedule a pipeline.
6) Deploy real-time Machine Learning Services with Azure Machine Learning
Learn how to register and deploy ML models with the Azure Machine Learning service.
In this module, you will learn how to:
In this module, you will learn how to:
- Deploy a model as a real-time inferencing service.
- Consume a real-time inferencing service.
- Troubleshoot service deployment.
7) Deploy Batch Inference Pipelines with Azure Machine Learning
Machine learning models are often used to generate predictions from large numbers of observations in a batch process. To accomplish this, you can use Azure Machine Learning to publish a batch inference pipeline.
Learning objectives:
Learning objectives:
- Learn how to create, publish, and use batch inference pipelines with Azure Machine Learning.
8) Tune Hyperparameters with Azure Machine Learning
Choosing optimal hyperparameter values for model training can be difficult, and usually involved a great deal of trial and error. With Azure Machine Learning, you can leverage cloud-scale experiments to tune hyperparameters.
Learning objectives:
Learning objectives:
- Learn how to use Azure Machine Learning hyperparameter tuning experiments to optimize model performance.
9) Automate Machine Learning Model Selection with Azure Machine Learning
Learn how to use automated machine learning in Azure Machine Learning to find the best model for your data.
In this module, you will learn how to:
In this module, you will learn how to:
- Use Azure Machine Learning's automated machine learning capabilities to determine the best performing algorithm for your data.
- Use automated machine learning to preprocess data for training.
- Run an automated machine learning experiment.
10) Explore Differential Privacy
Data scientists have an ethical (and often legal) responsibility to protect sensitive data. Differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values.
After completing this module, you'll be able to:
After completing this module, you'll be able to:
- Articulate the problem of data privacy.
- Describe how differential privacy works.
- Configure parameters for differential privacy.
- Perform differentially private data analysis.
11) Explain Machine Learning Models with Azure Machine Learning
Many decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions models make.
Learning objectives:
Learning objectives:
- Learn how to explain models by calculating and interpreting feature importance.
12) Detect and Mitigate Unfairness in Models with Azure Machine Learning
Machine learning models can often encapsulate unintentional bias that results in unfairness. With Fairlearn and Azure Machine Learning, you can detect and mitigate unfairness in your models.
In this module, you will learn:
In this module, you will learn:
- How to evaluate machine learning models for fairness.
- How to mitigate predictive disparity in a machine learning model.
13) Monitor Models with Azure Machine Learning
After a machine learning model has been deployed into production, it's important to understand how it is being used by capturing and viewing telemetry.
Learning objectives:
Learning objectives:
- Learn how to use Azure Application Insights to monitor a deployed Azure Machine Learning model.
14) Monitor Data Drift with Azure Machine Learning
Changing trends in data over time can reduce the accuracy of the predictions made by a model. Monitoring for this data drift is an important way to ensure your model continues to predict accurately.
Learning objectives:
Learning objectives:
- Learn how to monitor data drift in Azure Machine Learning.
15) Explore Security Concepts in Azure Machine Learning
Explore and experiment with securing a machine learning environment to ensure data remains private and models are accurate.
In this module, you will:
In this module, you will:
- Apply and understand Role-Based Access Control in Azure Machine Learning.
- Describe how secrets are managed in Azure Machine Learning.
- Use an Azure Machine Learning workspace with Azure Virtual Network.