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 to create, train, and deploy machine learning models. The program is designed so that 80% of the time is devoted to hands-on workshops and 20% to theory. Participants will learn how to leverage the full potential of the Azure Machine Learning platform through real-world examples and use cases.
What You Will Learn
- How to configure and manage an Azure ML Workspace environment.
- How to deploy ML models on Azure ML Service and monitor their performance.
- How to perform exploratory data analysis (EDA) 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 programming knowledge in Python.
- Experience with cloud services is a plus.
- Basic understanding of machine learning concepts.
Who is this training for?
Developers and data engineers seeking to expand their skills in cloud-based machine learning on Azure.-
IT specialists wanting to use Azure Machine Learning to automate data processing and predictive modeling.
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Data scientists and analysts looking to train and deploy ML models in a production environment.
Training Program
Day 1: Introduction to Azure Machine Learning and Basics
- Fundamentals of Azure Machine Learning
- Introduction to the Azure ecosystem and ML services
- Configuring the Azure ML Workspace environment
- Data preparation and exploratory data analysis (EDA)
- Importing and processing data in Azure
- Conducting EDA using Azure tools
- Training models in Azure
- Introduction to Azure ML Designer
- Automating model training with Azure AutoML
- Training your first model
- Hands-on exercises using a real dataset
- Analyzing results and evaluating model performance
Day 2: Advanced Techniques and Practical Applications
- Advanced model training techniques
- Using custom scripts for model training
- Leveraging GPUs and compute clusters to accelerate training
- Deploying models to Azure ML Service
- Monitoring and managing deployed models
- Model deployment and optimization
- Hands-on exercises for model deployment
- Hyperparameter tuning
- Model optimization strategies
