Azure Machine Learning Training
Level
IntermediateDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedAzure Machine Learning Training
This Azure Machine Learning course is an intensive two-day program focused on the practical use of Azure’s tools and services for building, training, and deploying machine learning models. The training is designed so that 80% of the time is spent on practical workshops and 20% on theory. Participants will learn how to leverage the full potential of the Azure Machine Learning platform by working on real-world examples and use cases.
What will you learn?
- How to configure and manage an 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 using Azure ML Designer and AutoML.
- How to integrate ML models with other Azure services such as Databricks and Cognitive Services.
Requirements
- Basic knowledge of Python programming.
- Experience with cloud services is an advantage.
- Foundational knowledge of machine learning.
Who is this training for?
Developers and data engineers who want to expand their skills in machine learning with Azure.-
IT specialists who want to use Azure Machine Learning for data processing automation and prediction.
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Data scientists and analysts looking to train and deploy ML models in production environments.
Training Program
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Day 1: Introduction to Azure Machine Learning and Basics
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Fundamentals of Azure Machine Learning
- Introduction to the Azure ecosystem and ML services
- Configuring the Azure ML 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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Training models in Azure
- Introduction to Azure ML Designer
- Automating model training with Azure AutoML
- Training the first model
- Hands-on exercises: training a model with a real dataset
- Analyzing results and evaluating the model
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Day 2: Advanced Techniques and Practical Applications
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Advanced model training techniques
- Using custom scripts to train models
- Leveraging GPUs and compute clusters to accelerate training
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Deploying and monitoring models
- Deploying models on Azure ML Service
- Monitoring and managing deployed models
- Model deployment and optimization
- Hands-on exercises: deploying a model
- Model optimization and hyperparameter tuning
