Learn to Design and Implement a Data Science Solution on Azure.
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.
Što ćete naučiti
- Doing Data Science on Azure
- Doing Data Science with Azure Machine Learning service
- Automate Machine Learning with Azure Machine Learning service
- Manage and Monitor Machine Learning Models with the Azure Machine Learning service
Kome je namijenjeno
- 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.
- A fundamental knowledge of Microsoft Azure
- Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
- Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
- Module 1: Introduction to 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.
- Getting Started with Azure Machine Learning
- Azure Machine Learning Tools
- Training Models with Designer
- Publishing Models with Designer
- Introduction to Experiments
- Training and Registering Models
- Working with Datastores
- Working with Datasets
- Working with Environments
- Working with Compute Targets
- Introduction to Pipelines
- Publishing and Running Pipelines
- Real-time Inferencing
- Batch Inferencing
- Hyperparameter Tuning
- Automated Machine Learning
- Introduction to Model Interpretation
- Using Model Explainers
- Monitoring Models with Application Insights
- Monitoring Data Drift
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