🚀 Starting a New Journey: Learning in Public
Instead of building projects by following tutorials, I want to understand how real-world AI data platforms are designed from scratch.
So, I'm starting a #LearningInPublic journey by building an End-to-End AI Data Engineering Platform.
Over the next few months, I'll explore every stage of the data lifecycle:
🔹 Data Ingestion (APIs, Files, Streaming)
🔹 Real-time & Batch Processing
🔹 Data Storage & Management
🔹 Machine Learning & LLMs
🔹 Interactive Dashboards
🔹 Kubernetes, Docker & CI/CD
I'll be sharing my daily learnings, architecture decisions, challenges, and lessons along the way.
The goal isn't just to build a project—it's to strengthen my fundamentals and understand how production-grade AI systems work.
Top comments (1)
Learning in public works well for data engineering because the architecture decisions are as useful as the final platform. I would pay special attention to lineage and failure logs early; AI features are much easier to trust when the data path is explainable.