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


Course Number: MOC-DP-100

Duration: 3 days (19.5 hours)

Format: Live, hands-on

Azure Training Overview

This Microsoft official course (DP-100), Designing and Implementing a Data Science Solution on Azure Training, teaches attendees how to operate machine learning solutions at cloud scale using Azure Machine Learning. Students learn how to leverage their 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.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

Objectives

  • 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
  • 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 their workspace
  • Use the Designer tool, a drag and drop interface for creating machine learning models without writing any code
  • Create a training pipeline that encapsulates data preparation and model training
  • Convert a training pipeline to an inference pipeline that can be used to predict values from new data
  • Deploy the inference pipeline as a service for client applications to consume
  • Do experiments that encapsulate data processing and model training code and use them to train machine learning models
  • Create and manage datastores and datasets in an Azure Machine Learning workspace and use them in model training experiments
  • Scale machine learning processes to an extent that would be infeasible on your own hardware
  • Manage experiment environments that ensure consistent runtime consistency for experiments
  • Create and use compute targets for experiment runs
  • Deploy models for real-time inferencing, and for batch inferencing
  • Use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for their data
  • Interpret models to explain how feature importance determines their predictions
  • Monitor models and their data

Prerequisites

Before attending this course, students must have:
  • Taken AZ-900: Azure fundamentals or have equivalent knowledge.
  • Experience of writing Python code to work with data, using libraries such as NumPy, Pandas, and Matplotlib. 
  • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or TensorFlow.

Outline

Expand All | Collapse All

Introduction to Azure Machine Learning
  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools
No-Code Machine Learning with Designer
  • Training Models with Designer
  • Publishing Models with Designer
Running Experiments and Training Models
  • Introduction to Experiments
  • Training and Registering Models
Working with Data
  • Working with Datastores
  • Working with Datasets
Compute Contexts
  • Working with Environments
  • Working with Compute Targets
Orchestrating Operations with Pipelines
  • Introduction to Pipelines
  • Publishing and Running Pipelines
Deploying and Consuming Models
  • Real-time Inferencing
  • Batch Inferencing
Training Optimal Models
  • Hyperparameter Tuning
  • Automated Machine Learning
Interpreting Models
  • Introduction to Model Interpretation
  • using Model Explainers
Monitoring Mode
  • Monitoring Models with Application Insights
  • Monitoring Data Drift
Conclusion

Training Materials:

All Microsoft Azure training students receive Microsoft official courseware.

Software Requirements:

Attendees will not need to install any software on their computer for this class. The class will be conducted in a remote environment that Accelebrate will provide; students will only need a local computer with a web browser with a stable Internet connection. Any recent version of Microsoft Edge, Mozilla Firefox, or Google Chrome will be fine.



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