Designing and Implementing a Data Science Solution on Azure
- Codice corso: DP-100T01-A
- Durata corso: 3gg
Introduzione
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.
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.
Prerequisiti
Before attending this course, students must have:
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.
Struttura del Corso
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
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
Lab : Creating an Azure Machine Learning Workspace
Lab : Working with Azure Machine Learning Tools
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 to consume.
Lessons
After completing this module, you will be able to:
Use designer to train a machine learning model
Deploy a Designer pipeline as a service
Lab : Creating a Training Pipeline with the Azure ML Designer
Lab : Deploying a Service with the Azure ML Designer
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
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
Lab : Running Experiments
Lab : Training and Registering 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
After completing this module, you will be able to:
Create and consume datastores
Create and consume datasets
Lab : Working with Datastores
Lab : Working with Datasets
MODULE 5: Compute Contexts
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
After completing this module, you will be able to:
Create and use Environments
Create and use Compute Targets
Lab : Working with Environments
Lab : Working with Compute Targets
MODULE 6: 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.
Lessons
After completing this module, you will be able to:
Create Pipelines to automate machine learning workflows
Publish and run Pipelines services
Lab : Creating a Pipeline
Lab : Publishing a Pipeline
MODULE 7: Deploying and Consuming Models
In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
Lessons
After completing this module, you will be able to:
Publish a model as a Real-time Inferenc service
Publish a model as a Batch inference service
Lab : Creating a Real-time Inferencing Service
Lab : Creating a Batch Inferencing Service
MODULE 8: Training Optimal Models
In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
Lessons
After completing this module, you will be able to:
Optimize hyperparameters for model training
Use automated machine learning to find the optimal model for your data
Lab : Tuning Hyperparameters
Lab : Using Automated Machine Learning
MODULE 9: Interpreting Models
This module describes how you can interpret models to explain how feature importance determines their predictions.
Lessons
After completing this module, you will be able to:;
Generate model explanations with automated machine learning
Use explainers to interpret machine learning models
Lab : Reviewing Automated Machine Learning Explanations
Lab : Interpreting Models
MODULE 10: Monitoring Models
This module describes techniques for monitoring models and their data.
Lessons
After completing this module, you will be able to:
Use Application Insights to monitor a published model
Monitor Data Drift
Lab : Monitoring a Model with Application Insights
Lab : Monitoring Data Drift