The Quantam Shift

About the Session:
Join AgileFever’s Free 1-Hour Live Masterclass on MLOps to understand how MLOps bridges the gap between Machine Learning and DevOps—enabling faster, scalable, and more reliable ML deployments.
🕒 When: October 27th | 11 AM EDT
🎯 Agenda Highlights:

• What is MLOps and why it matters
• Building an end-to-end MLOps pipeline
• Real-world challenges & best practices
• Open Q&A and expert insights
• Next steps to advance your MLOps journey

Key Takeaways:
Gain a clear understanding of the MLOps lifecycle, practical tools, and strategies to manage real-world ML workflows effectively.
👉 Register now and level up your MLOps skills!

1. Introduction
What is AI product architecture (intersection of product design + AI system design).
Difference between a traditional product architecture vs AI-driven product.
2. Core Components of AI Product Architecture
Front-end / UX Layer → how AI surfaces to users (chat, dashboard, APIs).
Application Layer → orchestration logic, agents, workflows.
AI/ML Layer → LLMs, embedding models, fine-tuning, vector DBs.
Data Layer → raw data, pipelines, feature stores.
Integration Layer → APIs, 3rd-party tools, monitoring.
Governance & Safety Layer → guardrails, ethics, security.
Why PMs & tech leads need to understand it
3. Architectural Patterns
RAG (Retrieval-Augmented Generation) vs Fine-tuning.
Agent-based architectures (multi-tool agents, planner-executor).
Hybrid architectures (LLM + traditional ML + rule-based).
Scaling considerations (token cost, latency, caching).
4. Demo to show prototyping of blueprint to finished AI product
Dify/Langflow → shows AI system architecture & flow.
Lovable → shows productization & user-facing app instantly.
5. Wrap-up & Takeaways
Checklist for designing AI product architecture:
What problem is AI solving?
Which architecture (RAG, agents, fine-tuned model) fits best?
What’s the data pipeline strategy?
How do we ensure reliability, explainability, and cost control?
Q&A.