DP-100: Designing and Implementing a Data Science Solution on Azure
Data professionals capture and analyze exponential amounts of data
Log in to EnrollSummary
- 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
Modules in this learning path
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Train and evaluate classification models
4 Units34 minClassification is a kind of machine learning used to categorize items into classes.
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Train and evaluate deep learning models
9 Units1 hr 4 minDeep learning is an advanced form of machine learning that emulates the way the human brain learns through networks of connected neurons.
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Train and evaluate clustering models
4 Units34 minClustering is a kind of machine learning that is used to group similar items into clusters.
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Train and evaluate regression models
4 Units34 minRegression is a commonly used kind of machine learning for predicting numeric values.
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Explore and analyze data with Python
4 Units29 minData exploration and analysis is at the core of data science. Data scientists require skills in languages like Python to explore, visualize, and manipulate data.
Modules in this learning path
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Detect and mitigate unfairness in models with Azure Machine Learning
7 Units45 minMachine 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.
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Introduction to Azure Machine Learning
8 Units42 minIntroduction to Azure Machine Learning
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Train a machine learning model with Azure Machine Learning
7 Units42 minLearn how to use Azure Machine Learning to train a model and register it in a workspace.
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Work with data in Azure Machine Learning
8 Units47 minLearn how to work with datastores and datasets in Azure Machine Learning.
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Use compute contexts in Azure Machine Learning
8 Units47 minLearn how to manage compute contexts for experiments in Azure Machine Learning.
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Create pipelines in Azure Machine Learning
10 Units57 minCreate pipelines in Azure Machine Learning
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Deploying machine learning models with Azure Machine Learning
7 Units42 minLearn how to register and deploy ML models with the Azure Machine Learning service.
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Automate machine learning model selection with Azure Machine Learning
7 Units42 minLearn how to use automated machine learning in Azure Machine Learning to find the best model for your data.
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Explore differential privacy
6 Units38 minExplore differential privacy
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Monitor data drift with Azure Machine Learning
6 Units42 minChanging 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.
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Monitor models with Azure Machine Learning
6 Units39 minAfter a machine learning model has been deployed into production, it’s important to understand how it is being used by capturing and viewing telemetry.
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Explain machine learning models with Azure Machine Learning
8 Units47 minMany 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.
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Tune hyperparameters with Azure Machine Learning
8 Units46 minChoosing 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.
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Deploy batch inference pipelines with Azure Machine Learning
6 Units44 minMachine 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.
Modules in this learning path
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Use automated machine learning in Azure Machine Learning
9 Units39 minTraining a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.
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Create a Regression Model with Azure Machine Learning designer
10 Units51 minRegression is a supervised machine learning technique used to predict numeric values. Learn how to create regression models using Azure Machine Learning designer.
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Create a classification model with Azure Machine Learning designer
10 Units55 minClassification is a supervised machine learning technique used to predict categories or classes. Learn how to create classification models using Azure Machine Learning designer.
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Create a Clustering Model with Azure Machine Learning designer
10 Units53 minClustering 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.
Modules in this learning path
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Explore the types of Azure Data Science Virtual Machines
5 Units25 minYou 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.
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Provision and use an Azure Data Science Virtual Machine
6 Units53 minProvision and use an Azure Data Science Virtual Machine
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Introduction to the Azure Data Science Virtual Machine
5 Units25 minAzure 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.
Modules in this learning path
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Analyze climate data with Azure Notebooks
8 Units45 minCreate an Azure Notebook and use three popular Python libraries to analyze climate data collected by NASA, then share it.
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Predict flight delays by creating a machine learning model in Python
6 Units51 minImport 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.
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Analyze the sentiment of reviews with Keras
5 Units35 minKeras 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.
Summary
- Length
- 3 days
- Level
- Intermediate
- Language
- English
About this course
Audience profile
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.