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.
Microsoft DP-100T01 - Designing and Implementing a Data Science Solution on Azure
- Length 3 days
- Version A
- Register interest
Why study this course
Aligns to certification
What you’ll learn
After completing this course, students will be able to:
Know how Azure services can support and augment the data science process
Use Azure Machine Learning service to automate the data science process end to end
Know about the machine learning pipeline and how the Azure Machine Learning service’s AutoML and HyperDrive can automate some of the laborious parts of it
Automatically manage and monitor machine learning models in the Azure Machine Learning service
Microsoft Azure at DDLS
DDLS is your best choice for training and certification in any of Microsoft’s leading technologies and services. We’ve been delivering effective training across all Microsoft products for over 30 years, and are proud to be Australia’s First and largest Microsoft Gold Learning Solutions Partner. All DDLS Microsoft courses follow Microsoft Official Curriculum (MOC) and are led by Microsoft Certified Trainers. Join more than 5,000 students who attend our quality Microsoft courses every year.
Expert instructors with real world experience and the latest vendor- approved in-depth course content.
Chosen and awarded by the world’s leading vendors as preferred training partner.
No matter your chosen technologies or platforms, we can help you stay one step ahead.
Who is the course for?
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.
We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please contact us via email on firstname.lastname@example.org
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
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.
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
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.
Introduction to Experiments
Training and Registering 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.
Working with Datastores
Working with Datasets
Lab : Working with Datastores
Lab : Working with 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 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
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 Operationalisation (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
Lab : Creating a Pipeline
Lab : Publishing a Pipeline
Module 7: 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.
Lab : Creating a Real-time Inferencing Service
Lab : Creating a Batch Inferencing Service
Module 8: 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 compute and find the best model for your data.
Automated Machine Learning
Lab : Tuning Hyperparameters
Lab : Using Automated Machine Learning
Module 9: Interpreting Models
Many of the decisions made by organisations and automated systems today are based on predictions made by machine learning models. It’s increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model’s behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.
Introduction to Model Interpretation
Using Model Explainers
Lab : Reviewing Automated Machine Learning Explanations
Lab : Interpreting Models
Module 10: Monitoring Models
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
Lab : Monitoring a Model with Application Insights
Lab : Monitoring Data Drift
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 visualise data.
Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
Terms & Conditions
The supply of this course by DDLS is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.