Building AI Applications That Leverage Data in Azure Blob Storage

Overview

Discover how Azure Blob Storage powers AI initiatives with secure, scalable storage for unstructured data, enabling seamless integration with Azure AI Services and optimized performance for enterprise applications.

Syllabus

• Introduction to Azure Blob Storage
Overview of Azure Blob Storage
Types of Blob Storage: Block, Append, and Page Blobs
Security features and access management
Pricing and cost management
• Storing and Managing Unstructured Data
Understanding unstructured data characteristics
Best practices for organizing and managing data
Data lifecycle and retention policies
• Integration with Azure AI Services
Overview of Azure AI Services
Connecting Blob Storage with Azure Cognitive Services
Leveraging Azure Machine Learning with Blob Storage
• Building AI Applications
Designing AI workflows with Azure Blob Storage
Data preprocessing and transformation techniques
Implementing AI models using Azure services
• Performance Optimization
Techniques for optimizing data retrieval and storage
Best practices for ensuring high availability and scalability
Monitoring and analyzing storage performance
• Security and Compliance
Understanding Azure security and compliance features
Implementing encryption and access controls
Compliance standards and certifications supported by Azure
• Case Studies and Real World Applications
Case study: Enterprise AI application using Blob Storage
Exploring real-world applications across industries
Lessons learned and best practices
• Hands-on Labs and Project Work
Setting up Azure Blob Storage environment
Integrating AI models with Blob Storage
Final project: Developing a complete AI application powered by Azure Blob Storage
• Conclusion and Next Steps
Recap of key concepts
Additional resources and learning paths
Certifications and career opportunities in AI and cloud storage

Subjects

Programming