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Designing and Implementing a Data Science Solution on Azure course DP-100
Learn how to operate machine learning solutions at cloud scale with Azure Machine Learning. This course teaches you how to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and monitoring machine learning solutions in Microsoft Azure. Designing and Implementing a Data Science Solution on Azure course DP-100
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks such as Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. AZ-220: Microsoft Azure IoT Developer
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.
Lessons
Getting Started with Azure Machine Learning
Azure Machine Learning Tools
Lab: Creating an Azure Machine Learning Workspace
Lab: Working with Azure Machine Learning Tools
After completing this module, you will be able to
Provision an Azure Machine Learning workspace
Use tools and code to work with Azure Machine Learning
Module 2: No-Code Machine Learning with Designer
This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications it consumes.
Lessons
Training Models with Designer
Publishing Models with Designer
Lab: Creating a Training Pipeline with the Azure ML Designer
Lab: Deploying a Service with the Azure ML Designer
After completing this module, you will be able to
Use designer to train a machine learning model
Deploy a Designer pipeline as a service
Module 3: 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.
Lessons
Introduction to Experiments
Training and Registering Models
Lab: Running Experiments
Lab: Training and Registering Models
After completing this module, you will be able to
Run code-based experiments in an Azure Machine Learning workspace
Train and register machine learning models
Module 4: 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.
Lessons
Working with Datastores
Working with Datasets
Lab: Working with Datastores
Lab: Working with Datasets
After completing this module, you will be able to
Create and consume datastores
Create and consume datasets
Module 5: Compute Contexts
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 impossible 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.
Lessons
Working with Environments
Working with Compute Targets
Lab: Working with Environments
Lab: Working with Compute Targets
After completing this module, you will be able to
Create and use environments
Create and use compute targets
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