Azure Machine Learning Service
Introduction to Azure Machine Learning
Azure Machine Learning is a cloud-based service for building, training, and deploying machine learning models. This module introduces Azure Machine Learning, covering its core features, architecture, and use cases for machine learning projects.
Getting Started with Azure Machine Learning
Learn how to create and configure an Azure Machine Learning workspace. This section includes setting up your environment, creating your first machine learning experiment, and understanding workspace components.
Data Preparation and Exploration
Discover techniques for data preparation and exploration using Azure Machine Learning. Learn about data ingestion, cleaning, transformation, and exploratory data analysis to prepare data for modeling.
Building and Training Models
Understand how to build and train machine learning models in Azure Machine Learning. Explore different algorithms, model training techniques, hyperparameter tuning, and evaluating model performance.
Deploying and Managing Models
Learn about deploying and managing machine learning models using Azure Machine Learning. Topics include creating deployment pipelines, publishing models as web services, and managing deployment environments.
Automated Machine Learning (AutoML)
Gain insights into Azure Machine Learning’s AutoML capabilities. Learn how to use AutoML to automatically select algorithms and hyperparameters, and to simplify the process of model training and selection.
Integration with Other Azure Services
Explore how to integrate Azure Machine Learning with other Azure services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. Understand how these integrations can enhance your machine learning workflows.
Monitoring and Managing Models
Discover how to monitor and manage the performance of deployed models. Learn about monitoring tools, tracking metrics, and managing model versions to ensure your models continue to deliver accurate results.
Security and Compliance
Learn about security and compliance considerations for Azure Machine Learning. Understand how to implement security measures, data protection strategies, and comply with industry standards and regulations.
Cost Management and Optimization
Understand cost management and optimization strategies for Azure Machine Learning. Learn about pricing models, cost control measures, and best practices for managing expenses while maintaining performance.
Real-World Projects and Case Studies
Review real-world projects and case studies that illustrate the use of Azure Machine Learning. Learn from practical examples of how organizations have implemented machine learning solutions to solve business problems.
Career Development and Azure Certifications
Explore Azure certifications related to machine learning and data science. Understand the skills required, career opportunities, and tips for certification preparation and interview success.
Azure Machine Learning Service Syllabus
1. Introduction to Azure Machine Learning Service
- Overview of Azure Machine Learning (AML)
- Key Concepts: Models, Experiments, Compute Targets
- Comparison with Azure Machine Learning Studio
2. Setting Up Azure Machine Learning Environment
- Creating an Azure Machine Learning Workspace
- Managing Workspaces and Resources
- Azure Machine Learning CLI and SDK
3. Azure Machine Learning Compute
- Understanding Compute Targets: VMs, GPU Instances, and AKS
- Configuring and Scaling Compute Instances
- Docker and Kubernetes Integration
4. Data Preparation and Management
- Data Stores and Datasets in Azure Machine Learning
- Data Preparation Techniques
- Data Versioning and Management
5. Azure Machine Learning Experiments
- Creating and Running Experiments
- Tracking Experiment Runs
- Experimentation Best Practices
6. Model Training and Deployment
- Training Models with Azure Machine Learning
- Hyperparameter Tuning and Automated ML
- Deploying Models as Web Services
7. Model Management and Monitoring
- Model Versioning and Deployment Profiles
- Model Performance Monitoring and Logging
- Model Retraining and Lifecycle Management
8. Integration with Azure Services
- Integrating Azure Databricks with Azure Machine Learning
- Using Azure Machine Learning Pipelines
- Integration with Azure DevOps for CI/CD
9. Advanced Machine Learning Techniques
- Deep Learning with Azure Machine Learning
- Reinforcement Learning Concepts
- Using Automated Machine Learning (AutoML)
10. Security and Compliance
- Role-Based Access Control (RBAC) for Azure Machine Learning
- Compliance and Governance Best Practices
- Secure Model Deployment
11. Monitoring and Optimization
- Monitoring Azure Machine Learning Services
- Optimization Techniques for Performance and Cost
- Scaling and Autoscaling Models
12. Advanced Topics and Case Studies
- Real-world Use Cases and Case Studies
- Advanced Analytics and Feature Engineering
- Interpretability and Explainability of Machine Learning Models
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details