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

Data professionals capture and analyze exponential amounts of data

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Summary

  • intermediate
  • azure
  • azure-machine-learning
  • azure-machine-learning-service
  • azure-machine-learning-studio
  • others
  • azure-data-science-vm
  • The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

Learning paths

3 hr 15 min
Machine learning is the foundation for predictive modeling and artificial intelligence. Learn some of the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models.

Modules in this learning path

  • Train and evaluate classification models
    4 Units
    34 min

    Classification is a kind of machine learning used to categorize items into classes.

  • Train and evaluate deep learning models
    9 Units
    1 hr 4 min

    Deep learning is an advanced form of machine learning that emulates the way the human brain learns through networks of connected neurons.

  • Train and evaluate clustering models
    4 Units
    34 min

    Clustering is a kind of machine learning that is used to group similar items into clusters.

  • Train and evaluate regression models
    4 Units
    34 min

    Regression is a commonly used kind of machine learning for predicting numeric values.

  • Explore and analyze data with Python
    4 Units
    29 min

    Data exploration and analysis is at the core of data science. Data scientists require skills in languages like Python to explore, visualize, and manipulate data.

5 hr 19 min
Azure Machine Learning is a cloud platform for creating and managing machine learning models. Learn how to apply your existing data science skills and build cloud-scale machine learning services that provide the foundation for artificial intelligence (AI) solutions.

Modules in this learning path

  • Detect and mitigate unfairness in models with Azure Machine Learning
    7 Units
    45 min

    Machine learning models can often encapsulate unintentional bias that results in unfairness. With Fairlearn and Azure Machine Learning, you can detect and mitigate unfairness in your models.

  • Introduction to Azure Machine Learning
    8 Units
    42 min

    Introduction to Azure Machine Learning

  • Train a machine learning model with Azure Machine Learning
    7 Units
    42 min

    Learn how to use Azure Machine Learning to train a model and register it in a workspace.

  • Work with data in Azure Machine Learning
    8 Units
    47 min

    Learn how to work with datastores and datasets in Azure Machine Learning.

  • Use compute contexts in Azure Machine Learning
    8 Units
    47 min

    Learn how to manage compute contexts for experiments in Azure Machine Learning.

  • Create pipelines in Azure Machine Learning
    10 Units
    57 min

    Create pipelines in Azure Machine Learning

  • Deploying machine learning models with Azure Machine Learning
    7 Units
    42 min

    Learn how to register and deploy ML models with the Azure Machine Learning service.

  • Automate machine learning model selection with Azure Machine Learning
    7 Units
    42 min

    Learn how to use automated machine learning in Azure Machine Learning to find the best model for your data.

  • Explore differential privacy
    6 Units
    38 min

    Explore differential privacy

  • Monitor data drift with Azure Machine Learning
    6 Units
    42 min

    Changing trends in data over time can reduce the accuracy of the predictions made by a model. Monitoring for this data drift is an important way to ensure your model continues to predict accurately.

  • Monitor models with Azure Machine Learning
    6 Units
    39 min

    After a machine learning model has been deployed into production, it’s important to understand how it is being used by capturing and viewing telemetry.

  • Explain machine learning models with Azure Machine Learning
    8 Units
    47 min

    Many 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 models make.

  • Tune hyperparameters with Azure Machine Learning
    8 Units
    46 min

    Choosing optimal hyperparameter values for model training can be difficult, and usually involved a great deal of trial and error. With Azure Machine Learning, you can leverage cloud-scale experiments to tune hyperparameters.

  • Deploy batch inference pipelines with Azure Machine Learning
    6 Units
    44 min

    Machine learning models are often used to generate predictions from large numbers of observations in a batch process. To accomplish this, you can use Azure Machine Learning to publish a batch inference pipeline.

3 hr 18 min
Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Learn how to use Azure Machine Learning to create and publish models without writing code.

Modules in this learning path

  • Use automated machine learning in Azure Machine Learning
    9 Units
    39 min

    Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.

  • Create a Regression Model with Azure Machine Learning designer
    10 Units
    51 min

    Regression is a supervised machine learning technique used to predict numeric values. Learn how to create regression models using Azure Machine Learning designer.

  • Create a classification model with Azure Machine Learning designer
    10 Units
    55 min

    Classification is a supervised machine learning technique used to predict categories or classes. Learn how to create classification models using Azure Machine Learning designer.

  • Create a Clustering Model with Azure Machine Learning designer
    10 Units
    53 min

    Clustering is an unsupervised machine learning technique used to group similar entities based on their features. Learn how to create clustering models using Azure Machine Learning designer.

1 hr 43 min
Azure includes a pre-configured virtual machine service for performing Data Science tasks. Learn how to use the Azure Data Science Virtual Machine to do common data analysis and machine learning tasks.

Modules in this learning path

  • Explore the types of Azure Data Science Virtual Machines
    5 Units
    25 min

    You learn about the types of Azure Data Science Virtual Machines (DSVM) and when to use each type. You will learn about the Windows-based and Linux-based DSVMs which each support different needs.

  • Provision and use an Azure Data Science Virtual Machine
    6 Units
    53 min

    Provision and use an Azure Data Science Virtual Machine

  • Introduction to the Azure Data Science Virtual Machine
    5 Units
    25 min

    Azure provides some pre-configured virtual machine images specifically designed for data science. Learn how you can use these to get a jump start on your data science work.

2 hr 11 min
Python has become a dominant language for doing data analysis with machine learning. Learn how to leverage Python and associated libraries in Jupyter Notebooks run on Azure Notebooks to predict patterns and identify trends.

Modules in this learning path

  • Analyze climate data with Azure Notebooks
    8 Units
    45 min

    Create an Azure Notebook and use three popular Python libraries to analyze climate data collected by NASA, then share it.

  • Predict flight delays by creating a machine learning model in Python
    6 Units
    51 min

    Import airline arrival data into a Jupyter notebook and use Pandas to clean it. Then, build a machine learning model with Scikit-Learn and use Matplotlib to visualize output.

  • Analyze the sentiment of reviews with Keras
    5 Units
    35 min

    Keras is a high-level neural networks API, written in Python, that runs on top of other deep learning tools such as TensorFlow. This module uses Keras to build a neural network that scores text, such as user reviews for sentiment.

Additional courses

The learning paths above prepare you for the knowledge and skills needed to pass the exam and become certified. Enrolling in this track also enrolls you in the Microsoft Official Classroom course below. You can use this course as an extra reference to prepare for the exam.

Designing and Implementing a Data Science Solution on Azure

Summary

Length
3 days
Level
Intermediate
Language
English

About this 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.

Audience profile

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

Prerequisites

  • Azure fundamentals
  • Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
  • How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.