Designing and Implementing a Data Science Solution on Azure (DP-100)

Course 8532

  • Duration: 4 days
  • Exam Voucher: Yes
  • Language: English
  • Level: Intermediate

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This Azure Data Science Certification 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 with Azure Machine Learning and MLflow.

Azure Data Science Certification Training Delivery Methods

  • In-Person

  • Online

Azure Data Science Certification Training Information

In this course, you will learn how to:

  • Design machine learning solutions.
  • Create and manage assets and resources in the Azure Machine Learning workspace, using the portal, the studio, the Azure CLI, and especially the Python SDK (v2).
  • Build and run pipelines with the no-code designer in the Azure Machine Learning studio.
  • Use Automated Machine Learning to explore featurisation and algorithms.
  • Train and track machine learning models in Azure Machine Learning notebooks using MLflow.
  • Train and track machine learning models using scripts as Azure Machine Learning jobs, using MLflow.
  • Create, run, and schedule Azure Machine Learning pipelines.
  • Deploy models to real-time and batch endpoints.
  • Apply Responsible AI principles to data, models, and model training.
  • Design a MLOps solution and design for monitoring and retraining.

Prerequisites

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 visualize data. 
  • Working with containers.
  • Training and validating machine learning models using frameworks like Scikit-Learn, PyTorch, and TensorFlow. 

If you are entirely 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 Training Outline

Learn how to design a data ingestion solution for training data used in machine learning projects. 

In this module, you'll learn how to: 

  • Identify your data source and format 
  • Choose how to serve data to machine learning workflows 
  • Design a data ingestion solution 

Learn how to design a model training solution for machine learning projects. 

In this module, you'll learn how to: 

  • Identify machine learning tasks 
  • Choose a service to train a model 
  • Choose between compute options

Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model. 

In this module, you'll learn how to: 

  • Understand how a model will be consumed. 
  • Decide whether to deploy your model to a real-time or batch endpoint. 

As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets. 

In this module, you'll learn how to: 

  • Create an Azure Machine Learning workspace. 
  • Identify resources and assets. 
  • Train models in the workspace. 

Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2). 

In this module, you'll learn how and when to use: 

  • The Azure Machine Learning studio. 
  • The Python Software Development Kit (SDK). 
  • The Azure Command Line Interface (CLI). 

Learn about how to connect to data from the Azure Machine Learning workspace. You'll be introduced to datastores and data assets. 

In this module, you'll learn how to: 

  • Work with Uniform Resource Identifiers (URIs). 
  • Create and use datastores. 
  • Create and use data assets.

Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster. 

In this module, you'll learn how to: 

  • Choose the appropriate compute target. 
  • Create and use a compute instance. 
  • Create and use a compute cluster. 

Learn how to use environments in Azure Machine Learning to run scripts on any compute target. 

In this module, you'll learn how to: 

  • Understand environments in Azure Machine Learning. 
  • Explore and use curated environments. 
  • Create and use custom environments. 

Learn how to find the best classification model with automated machine learning (AutoML). You'll use the Python SDK (v2) to configure and run an AutoML job. 

In this module, you'll learn how to: 

  • Prepare your data to use AutoML for classification. 
  • Configure and run an AutoML experiment. 
  • Evaluate and compare models. 

Learn how to use MLflow for model tracking when experimenting in notebooks. 

In this module, you'll learn how to: 

  • Configure to use MLflow in notebooks 
  • Use MLflow for model tracking in notebooks 

Learn how to convert your code to a script and run it as a command job in Azure Machine Learning. 

In this module, you'll learn how to: 

  • Convert a notebook to a script. 
  • Test scripts in a terminal. 
  • Run a script as a command job. 
  • Use parameters in a command job. 

Learn how to track model training with MLflow in jobs when running scripts. 

In this module, you learn how to: 

  • Use MLflow when you run a script as a job. 
  • Review metrics, parameters, artifacts, and models from a run. 

Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows. 

In this module, you'll learn how to: 

  • Create components. 
  • Build an Azure Machine Learning pipeline. 
  • Run an Azure Machine Learning pipeline. 

Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning. 

In this module, you'll learn how to: 

  • Define a hyperparameter search space. 
  • Configure hyperparameter sampling. 
  • Select an early-termination policy. 
  • Run a sweep job. 

Learn how to deploy models to a managed online endpoint for real-time inferencing. 

In this module, you'll learn how to: 

  • Use managed online endpoints. 
  • Deploy your MLflow model to a managed online endpoint. 
  • Deploy a custom model to a managed online endpoint. 
  • Test online endpoints. 

Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you'll trigger a batch scoring job. 

In this module, you'll learn how to: 

  • Create a batch endpoint. 
  • Deploy your MLflow model to a batch endpoint. 
  • Deploy a custom model to a batch endpoint. 
  • Invoke batch endpoints.

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Azure Data Science Certification Training FAQs

Azure Data Scientists work with data stored in Azure data storage solutions, which they access from Azure Machine Learning. They work with notebooks and scripts to train machine learning models and use Azure Machine Learning managed compute for their workloads.

Azure Data Scientists should primarily focus on being proficient at using the Python SDK (v2) to interact with the Azure Machine Learning workspace, and MLflow to track and manage models. However, they should also be familiar with the Studio and Azure CLI tools 

This course is for Azure Data Scientists. Azure Data Scientists are expected to perform data science tasks on Azure. The course doesn’t teach data science but expects the audience to already be familiar with the basic data science and machine learning concepts.

The Azure Data Science Certification Training course teaches participants how to operate machine learning solutions at cloud scale using Azure Machine Learning. The course covers topics such as data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

The course is available as both in-person and online training.

If you are completely new to data science and machine learning, it is recommended that you complete the Learning Tree course 8580, Microsoft Azure AI Fundamentals Training (AI-900) before taking this course. 

In the course, you will learn how to use Azure services to develop machine learning solutions, deploy machine learning models, automate machine learning with Azure Machine Learning service, and manage and monitor machine learning models with the Azure Machine Learning service.

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

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

Yes, the course can be brought to your organisation and delivered when, where, and how you want it. The content can also be tailored to meet the specific needs of your organisation.

Please reach out to info@learningtree.com after your course to obtain your exam voucher.