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



Microsoft offers the famous Designing and Implementing a Data Science Solution on Azure (DP-100) Certification Training. Professionals who are data scientists and have a solid understanding of programming languages like Python and machine learning frameworks like Tensorflow, Pytorch, and others are the best candidates for this DP-100 training course. Check out the dates shown below to choose the best time for you to enroll and earn your Azure Data Scientist Associate certification.

In the past ten years, Microsoft Azure has rapidly increased its market share and become one of the most technology advancements in the corporate environment internationally. Microsoft Azure is used by around 80% of Fortune 500 firms to deliver services and solutions to customers all over the world thanks to its comprehensive cloud capabilities.

Today's professionals who work as data scientists need a vital set of abilities, including the ability to create data solutions on Azure. It is crucial to understand Microsoft Azure well, as well as how to build and deploy data solutions for it, as more and more services and solutions are going to the cloud. An industry-recognized course completion certificate and a copy of the course materials are provided to participants in this Designing and Implementing a Data Solution on Azure DP-100 training from a Microsoft Gold Partner.

Course Objectives

At the end of this course the participants will be able to:

  • Create machine learning solutions with Azure services.
  • Use Azure for data science operations.
  • Acquire a working knowledge of Azure machine learning automation.
  • Use Azure machine learning, manage and monitor machine learning models.

Targeted Audience

  • Data Scientists
  • Machine Learning professionals
  • Professionals who create data solutions for Microsoft Azure
  • Anybody who wants to understand Implementing an Azure Data Solution

Course Outlines

Unit 1: Getting started with Azure Machine Learning & Visual Tools for Machine Learning

  • Azure Machine Learning Introduction
  • Working with Azure Machine Learning
  • Automated Machine Learning
  • Azure Machine Learning Designer

Unit 2: Running Experiments and Training Models & Working with Data

  • Introduction to Experiments
  • Training and Registering Models
  • Working with Datastores
  • Working with Datasets

Unit 3: Working with Compute & Orchestrating Machine Learning Workflows

  • Environments
  • Compute Targets
  • Introduction to Pipelines
  • Publishing and Running Pipelines

Unit 4: Deploying and Consuming Models & Training Optimal Models

  • Real-time Inferencing
  • Batch Inferencing
  • Continuous Integration and Delivery
  • Hyperparameter Tuning
  • Automated Machine Learning

Unit 5: Responsible Machine Learning & Monitoring Models

  • Differential Privacy
  • Model Interpretability
  • Fairness
  • Monitoring Models with Application Insights
  • Monitoring Data Drift

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