DP-100T01 Designing and Implementing a Data Science Solution on Azure

Price
$1,785.00
Duration
 3 Days
Delivery Methods
 VILT    Private Group
Eligible UT Programs: Learning Credits, Coupons, Technical Training Pass, Training Passport

The Azure Data Scientist applies their knowledge of data science and machine learning to implementing and running machine learning workloads on Microsoft 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.

 

Upcoming Class Dates and Times

Jan 16  - Jan 18, 2023
8:00AM - 4:00PM Central
3 Days
Virtual Instructor Led
 GTR
May 01  - May 03, 2023
8:00AM - 4:00PM Central
3 Days
Virtual Instructor Led
 GTR

Who Should Attend

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Course Objectives

    Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This 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 in Microsoft Azure.

Agenda

1 - Getting Started with Azure Machine Learning
  • Introduction to Azure Machine Learning
  • Working with Azure Machine Learning
2 - Visual Tools for Machine Learning
  • Automated Machine Learning
  • Azure Machine Learning Designer
3 - Running Experiments and Training Models
  • Introduction to Experiments
  • Training and Registering Models
4 - Working with Data
  • Working with Datastores
  • Working with Datasets
5 - Working with Compute
  • Working with Environments
  • Working with Compute Targets
6 - Orchestrating Operations with Pipelines
  • Introduction to Pipelines
  • Publishing and Running Pipelines
7 - Deploying and Consuming Models
  • Real-time Inferencing
  • Batch Inferencing
  • Continuous Integration and Delivery
8 - Training Optimal Models
  • Hyperparameter Tuning
  • Automated Machine Learning
9 - Responsible Machine Learning
  • Differential Privacy
  • Model Interpretability
  • Fairness
10 - Monitoring Models
  • Monitoring Models with Application Insights
  • Monitoring Data Drift

Prerequisites

  • Creating cloud resources in Microsoft Azure.
  • Using Python to explore and visualize data.
  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
  • Working with containers
  • AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.
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