Designing and implementing a data science solution on Azure

  • DP-100

Learn new skills and discover the power of Microsoft Azure with step-by-step guidance.

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The DP-100 course is designed for data scientists who want to build, deploy, and manage machine learning solutions on Microsoft Azure.


This course covers the end-to-end process of implementing AI and ML solutions, from data preparation to model training and deployment using Azure Machine Learning.


Learners will gain hands-on experience in using Azure ML services, automating ML workflows, and leveraging cloud-based AI capabilities.


Course Overview
  • Course : Designing and implementing a data science solution on Azure
  • Level : Intermediate
  • Type : Instructor led Live online or Classroom
  • Length : 4 days
  • Labs : Included
  • Practice Test : Included
Who should take this course
  • Data scientists and AI professionals
  • Machine learning engineers
  • IT professionals and developers
  • Anyone preparing for the Microsoft Certified: Azure Data Scientist Associate certification.
Prerequisites
  • Experience with Python programming.
  • Understanding of data processing and analysis techniques.
  • Familiarity with cloud computing concepts, particularly Microsoft Azure.
What you'll learn
  • Understand and implement Azure Machine Learning
  • Train and optimize machine learning models using Azure ML SDK and AutoML.
  • Deploy and manage models using Azure ML Pipelines
  • Perform data ingestion, transformation, and feature engineering
  • Apply MLOps best practices for automating machine learning workflows.
  • Monitor and troubleshoot deployed models for performance and scalability.
  • And more
Training Mode

Instructor-led Virtual online live Training.

  • Weekend Classes
  • Evening Classes
  • Weekdays Classes

Course Outline


Module 1: Explore and configure the Azure Machine Learning workspace

  • Explore Azure Machine Learning workspace resources and assets
  • Explore developer tools for workspace interaction
  • Make data available in Azure Machine Learning
  • Work with compute targets in Azure Machine Learning
  • Work with environments in Azure Machine Learning

Module 2: Experiment with Azure Machine Learning

  • Preprocess data and configure featurization
  • Run an Automated Machine Learning experiment
  • Evaluate and compare models

Module 3: Optimize model training with Azure Machine Learning

  • Run atraining script as a command job in Azure Machine Learning
  • Track model training with MLflow in jobs
  • Perform hyperparameter tuning with Azure Machine Learning
  • Run pipelines in Azure Machine Learning

Module 4: Manage and review models in Azure Machine Learning

  • Register an MLflow model in Azure Machine Learning
  • Create and explore the Responsible AI dashboard
  • Machine Learning

Module 5: Deploy and consume models with Azure Machine Learning

  • Deploy a model to a managed online endpoint
  • Deploy a model to a batch endpoint

Module 6: Develop generative AI apps in Azure AI Foundry portal

  • Introduction to Azure AI Foundry
  • Explore and deploy models from the model catalog in Azure AI Foundry portal
  • Get started with prompt flow to develop language model apps in the Azure AI Foundry
  • Build a RAG-based agent with your own data
  • Fine-tune a language model with Azure AI Foundry
  • Evaluate the performance of generative AI apps
  • Responsible generative AI

Hands-on Labs

  • Lab : Creating an Azure Machine Learning Workspace
  • Lab : Use Automated Machine Learning
  • Lab : Use Azure Machine Learning Designer
  • Lab : Run Experiments
  • Lab : Train Models
  • Lab : Work with Data
  • Lab : Work with Compute
  • Lab : Create a Pipeline
  • Lab : Create a Real-time Inferencing Service
  • Lab : Create a Batch Inferencing Service
  • Lab : Tune Hyperparameters
  • Lab : Use Automated Machine Learning from the SDK
  • Lab : Explore Differential privacy
  • Lab : Interpret Models
  • Lab : Detect and Mitigate Unfairness
  • Lab : Monitor a Model with Application Insights
  • Lab : Monitor Data Drift

Best Value offer

  • Official Courseware
  • Instructor-led live online / Classroom
  • Flexible Schedule
  • Course Completion Certification


US$ 2589


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