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