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

Level: Intermediate

Prepare for the official Microsoft Azure Data Scientist Associate certification exam DP-100 in this Designing and Implementing a Data Science Solution on Azure course. Gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline. This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.

Key Features of this Azure Data Science Certification Training:

  • Microsoft Official Course content

You Will Learn How To:

  • Use Azure to 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

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  • 3-day instructor-led training course
  • One-on-one after course instructor coaching
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In Class & Live, Online Training

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Note: This course runs for 3 Days

  • Mar 30 - Apr 1 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Reserve Your Seat

  • Apr 28 - 30 9:00 AM - 4:30 PM EDT New York / Online (AnyWare) New York / Online (AnyWare) Reserve Your Seat

  • May 19 - 21 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

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Important Azure Data Science Certification Course Information

  • Requirements

    Before attending this course, students must have:

    • Azure Fundamentals knowledge.
    • An understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
    • Python programming language experience and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.
  • Who Should Attend This Course

    This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.

  • Microsoft Exam 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

  • 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

    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 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

    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.

    • 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.

    • 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 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

    After completing this module, you will be able to

    • Create and use environments
    • Create and use 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.

    • 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
  • 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.

    • 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
  • 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.

    • Hyperparameter Tuning
    • Automated Machine Learning
    Lab : Tuning Hyperparameters Lab : Using Automated Machine Learning

    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
  • Module 9: Interpreting Models

    Many of the decisions made by organizations 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

    After completing this module, you will be able to

    • Generate model explanations with automated machine learning
    • Use explainers to interpret machine learning 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

    After completing this module, you will be able to

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

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Azure Data Science Certification FAQs

  • What is required to achieve the Microsoft Certified: Azure Data Scientist Associate certification?

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