Integrating AI into the Product Architecture

Course details

This course equips developers, machine learning engineers, and AI engineers with the essential knowledge and practical skills to seamlessly integrate AI models into product architectures. Explore effective collaboration techniques with data science teams, best practices for comprehensive model testing and validation, and proven architectural patterns for scaling and deploying AI-powered applications. The course also covers crucial UX/UI considerations to build trustworthy, interpretable, and ethical AI-infused products.

Learning objectives
Learn to apply robust testing methodologies to ensure model performance, robustness, and fairness.
Build scalable and maintainable AI integration patterns tailored to product requirements.
Implement comprehensive monitoring and observability for AI models in production.
Incorporate principles of explainable AI, uncertainty handling, and ethical AI practices into the user experience.

Concepts

Welcome to the Course

01 - Course introduction and prerequisites

1. Foundations of LLM Integration

02 - What LLMs can (and can't) do in production
03 - Deep dive into LLMs - Recap of the mechanics
04 - Prompt engineering techniques to improve LLM output
05 - Cross-functional team for LLM integration

2. Integrating LLMs into Your Product

06 - Choosing your LLM provider - Navigating the LLM landscape
07 - API-based access - Simplifying LLM integration
08 - Hands-on - your first LLM integration
09 - Sync vs. Async - Integrating LLMs effectively
10 - Real-time data pipelines for LLM integration
11 - Secure LLM integration - API keys, rate limiting, and data access

3. Deployment Strategies

12 - Understanding deployment options
13 - Cloud-based LLMs - Advantages and disadvantages
14 - On-premise LLMs - Control and customization
15 - Edge deployment - Low latency and privacy
16 - Hybrid deployment - Balancing flexibility and complexity
17 - Choosing the right deployment strategy

4. Optimizing Performance and Reliability

18 - Fine-tuning overview - Tailoring LLMs to specific needs
19 - Caching LLM responses - Optimizing performance and cost
20 - Handling LLM failures - Building a reliable retry system
21 - Scaling - Architectural patterns for LLM integration

5. Production Readiness - Testing, Validation, and Monitoring

22 - Validating and sanitizing LLM inputs and outputs
23 - Testing your LLM integration - Strategies and best practices
24 - Key metrics for LLM integration - What to track
25 - Setting up alerts - Monitoring your LLM integration
26 - Optimizing LLM costs in production

6. User Experience, Ethics, and Governance

27 - Graceful degradation and transparent communication
28 - Explainable AI - Providing insights into LLM decisions
29 - Ethical considerations - Bias, fairness, and responsible use
30 - LLM usage tracking and compliance

Conclusion

31 - LLMs for business growth - Revisiting the potential
32 - Course summary and future learning