AI to the Masses! But How? Pixie Dust?

Overview

Explore Azure's AI options for data science, weighing considerations and practical applications to make informed decisions about implementing AI in your organization.

Syllabus

• Introduction to AI and Azure AI
Overview of Artificial Intelligence: Concepts and Applications
Introduction to Azure AI Platform: Services and Tools Available
• Understanding Azure AI Services
Cognitive Services: Vision, Speech, Language, and Search Capabilities
Azure Machine Learning: Building, Training, and Deploying Models
Azure Bot Services: Creating Intelligent Agents
• Data Science in Azure
Data Engineering with Azure: Data Storage and Processing Services
Exploratory Data Analysis: Tools and Techniques in Azure
Advanced Analytics: Leveraging Azure for Predictive and Prescriptive Analytics
• Practical Considerations for AI Implementation
Assessing Business Needs: Identifying AI Opportunities in Your Organization
Ethical and Responsible AI: Principles and Best Practices
Security and Privacy: Safeguarding Data in AI Applications
• Case Studies and Real-world Applications
Successful AI Deployments: Industry Examples and Outcomes
Lessons Learned from AI Implementations: Challenges and Solutions
• Hands-on Labs and Projects
Setting Up Your Azure Environment: Getting Started with Azure AI
Building a Simple AI Application: Step-by-step Guide
Advanced Project: Integrating Multiple Azure AI Services for a Complete Solution
• Decision Making in AI Strategy
Evaluating AI Options: Cost, Scalability, and Performance Considerations
Creating an AI Roadmap: Strategic Planning for AI Deployment
• Conclusion and Future of AI
Emerging Trends in AI: What's Next for Azure AI
Q&A Session: Addressing Participant Questions and Feedback

Subjects

Conference Talks