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Python ProgrammingDesigning 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.
Demand for Cloud Skills
Learn core Azure services and cloud concepts for career success. Start from the basics and progress to cloud expertise.
Hands-On Learning
Understanding Azure allows you to leverage technology to stay ahead of competitors and adapt to market changes.
Learn from Azure Experts
Build a solid foundation in cloud computing with Azure and lead in an increasingly digital business landscape.
- Instructor-led
- Official Courseware
- Flexible Schedule
- Guaranteed To Run
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 : 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
- Data scientists and AI professionals
- Machine learning engineers
- IT professionals and developers
- Anyone preparing for the Microsoft Certified: Azure Data Scientist Associate certification.
- Experience with Python programming.
- Understanding of data processing and analysis techniques.
- Familiarity with cloud computing concepts, particularly Microsoft Azure.
- 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
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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