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 in Microsoft Azure.
At the end of Designing Implementing Microsoft Azure Networking Solutions (AZ-700) training course, participants will learn
Design, implement and manage hybrid network connections
Design and implement core Azure networking infrastructure
Design and implement routing and load balancing in Azure
Secure and monitor networks
Design and implement private access to Azure Services
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.
- 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
Getting Started with Azure Machine Learning
In this module, you will learn how to provision an Azure Machine Learning workspace and use it to
manage machine learning assets such as data, compute, model training code, logged metrics, and
trained models. You will learn how to use the web-based Azure Machine Learning studio interface
as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter
Notebooks to work with the assets in your workspace.
- Introduction to Azure Machine Learning
- Working with Azure Machine Learning
Visual Tools for Machine Learning
This module introduces the Automated Machine Learning and Designer visual tools, which you can
use to train, evaluate, and deploy machine learning models without writing any code.
- Automated Machine Learning
- Azure Machine Learning Designer
Running Experiments and Training Models
In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
- Introduction to Experiments
- Training and Registering Models
Working with Data
Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
- Working with Datastores
- Working with Datasets
Working with Compute
One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
- Working with Environments
- Working with Compute Targets
Orchestrating Operations with Pipelines
Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.
- Introduction to Pipelines
- Publishing and Running Pipelines
Deploying and Consuming Models
Models are designed to help decision-making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
- Real-time Inferencing
- Batch Inferencing
- Continuous Integration and Delivery
Training Optimal Models
By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models, but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale computing and find the best model for your data.
- Hyperparameter Tuning
- Automated Machine Learning
Responsible Machine Learning
Data scientists have a duty to ensure they analyze data and train machine learning models
responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module
explores some considerations and techniques for applying responsible machine learning principles.
- Differential Privacy
- Model Interpretability
After a model has been deployed, it’s important to understand how the model is being used in production and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
- Monitoring Models with Application Insights
- Monitoring Data Drift