Azure Data Science Certification Training (DP-100)

Course 8532

  • Duration: 3 days
  • Language: English
  • Level: Intermediate

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.

DP-100 Training Delivery Methods

  • In-Person

  • Online

DP-100 Training Course Information

In this Azure data scientist certification training, you will learn the following:

  • Use Azure services to develop machine learning solutions.
  • Deploy machine learning models.
  • Automate Machine Learning with Azure Machine Learning service.
  • Manage and Monitor Machine Learning Models with the Azure Machine Learning service.

Prerequisites

If you are completely new to data science and machine learning, please complete Learning Tree course 8580, Microsoft Azure AI Fundamentals Training (AI-900).

Certification Information

This course can help you prepare for the following Microsoft role-based certification exam — DP-100: Designing and Implementing a Data Science Solution on Azure.

Azure Data Science Certification Course Outline

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. In addition, you will learn how to use the web-based Azure Machine Learning studio interface and the Azure Machine Learning SDK, and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

  • Introduction to Azure Machine Learning
  • Working with Azure Machine Learning

Lab: Creating an Azure Machine Learning Workspace

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.

This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.

Lessons

  • Automated Machine Learning
  • Azure Machine Learning Designer

Lab: Use Automated Machine Learning

Lab: Use Azure Machine Learning Designer

After completing this module, you will be able to:

  • Use automated machine learning to train a machine learning model.
  • Use Azure Machine Learning designer to train a model.

In this module, you will start with experiments that encapsulate data processing and model training code and use them for training 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.

Data is a fundamental element in any machine learning workload. In this module, you will learn how to create and manage data stores 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.

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 the extent that would be infeasible on your 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.

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 vital 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.

Lessons

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Lab: Creating a Pipeline

Lab: Publishing a Pipeline

After completing this module, you will be able to:

  • Create pipelines to automate machine learning workflows.
  • Publish and run pipeline services.

Models are designed to help decision-making through predictions, so they're only helpful when deployed and available for an application to consume. In this module, learn how to deploy models for real-time inferencing and batch inferencing.

Lessons

  • Real-time Inferencing
  • Batch Inferencing

Lab: Creating a Real-time Inferencing Service

Lab: Creating a Batch Inferencing Service

After completing this module, you will be able to:

  • Publish a model as a real-time inference service.
  • Publish a model as a batch inference service.

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 to use hyperparameter tuning and automated machine learning to take advantage of cloud-scale computing and find the best model for your data.

Lessons

  • Hyperparameter Tuning
  • Automated Machine Learning

Lab: Tuning Hyperparameters

Lab: Using Automated Machine Learning

After completing this module, you will be able to:

  • Optimise hyperparameters for model training.
  • Use automated machine learning to find the optimal model for your data.

Data scientists must ensure they analyse data and train machine learning models responsibly, respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine-learning principles.

Lessons

  • Differential Privacy
  • Model Interpretability
  • Fairness

Lab: Explore Differential privacy

Lab: Interpreting Models

Lab: Detect and Mitigate Unfairness

After completing this module, you will be able to:

  • Apply differential privacy to data analysis.
  • Use explainers to interpret machine learning models.
  • Evaluate models for fairness.

After a model has been deployed, it's essential to understand how it is 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.

Lessons

  • Monitoring Models with Application Insights
  • Monitoring Data Drift

Lab: Monitoring a Model with Application Insights

Lab: Monitoring Data Drift

After completing this module, you will be able to:

  • Use Application Insights to monitor a published model.
  • Monitor data drift.

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DP-100 Training FAQs

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.

Specifically:

  • 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
  • Working with containers

Attend this course and get prepped to pass Exam DP-100 to achieve Azure Data Scientist Associate certification.

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.