Azure Machine Learning Training
Level
IntermediateDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedAzure Machine Learning Training
This intensive two-day course focuses on the practical use of tools and services available in Azure for creating, training, and deploying machine learning models. The program is designed so that 80% of the time is dedicated to practical workshops and 20% to theory. Participants will learn how to leverage the full potential of Azure Machine Learning through real-world examples and use cases.
What You Will Learn
- How to configure and manage the Azure ML Workspace
- How to deploy ML models on Azure ML Service and monitor their performance
- How to conduct exploratory data analysis and train ML models with Azure ML Designer and AutoML
Who is this training for?
Developers and data engineers who want to expand their skills in machine learning in the Azure cloud-
IT specialists looking to use Azure Machine Learning for data processing automation and prediction
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Data scientists and data analysts who want to train and deploy ML models in production environments
Training Program
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Day 1: Introduction to Azure Machine Learning and Fundamentals
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Azure Machine Learning Basics
- Introduction to the Azure ecosystem and machine learning services
- Configuring the Azure Machine Learning Workspace
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Data Preparation and Exploratory Data Analysis (EDA)
- Importing and processing data in Azure
- Performing EDA using Azure tools
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Model Training in Azure
- Introduction to Azure ML Designer
- Automating model training with Azure AutoML
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Training the First Model
- Hands-on: training a model on a real dataset
- Result analysis and model evaluation
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Day 2: Advanced Techniques and Practical Applications
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Advanced Model Training Techniques
- Using custom scripts for model training
- Leveraging GPUs and compute clusters for faster training
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Deploying and Monitoring Models
- Deploying models on Azure Machine Learning Service
- Monitoring and managing deployed models
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Deployment and Model Optimization
- Practical exercises in model deployment
- Model optimization and hyperparameter tuning
