Stay updated with Data & AI News!

Join The Ravit Show Newsletter

Flink Forward Asia 2025 Key Takeaways, 10 Github Open Source Repos for AI Engineering Careers | The Ravit Show

Flink Forward Asia 2025 Key Takeaways, 10 Github Open Source Repos for AI Engineering Careers

Ravit Jain
August 18, 2025

Inside Flink Forward Asia 2025: Real-Time AI Is Here

I had the chance to attend Flink Forward Asia in person this year in Singapore—and it was one of those events that made it clear just how far the real-time data ecosystem has come.

What stood out immediately was the pace of innovation from the Apache Flink community.
The announcements weren’t just incremental. They signaled a shift in how streaming, batch, and AI are starting to converge:

Apache Flink 2.x introduced disaggregated state management and built-in SQL-level AI functions—validated by VLDB 2025
Flink Agents debuted as a new framework for building agentic AI based on events and system triggers. It’s a bold step toward making AI systems more context-aware and autonomous
Apache Paimon expanded into multi-modal support, becoming a high-performance lakehouse for both streaming and batch workloads
Apache Fluss officially entered incubation at the Apache Software Foundation—built for the next generation of real-time analytics and AI workloads

Beyond the keynotes, the practitioner talks were grounded in experience. Speakers from companies like TikTok, LinkedIn, Grab, Lazada, Salesforce, and Shopee shared practical lessons from scaling sub-second pipelines in production.

Hallway conversations were just as engaging—deep dives into stateful processing, AI integration, and building high-performance pipelines across hybrid stacks. The takeaway was simple: real-time AI isn’t just possible, it’s already driving core services across industries.

This wasn’t just a showcase of what’s new. It felt like a roadmap for where Flink and real-time AI are heading—especially across Asia.

If you want to catch up on what was shared, the full set of session recordings and slide decks are available here:

Flink Forward Asia 2025 Highlights

A clear path into AI engineering using 10 GitHub repos

Repos and links

ML for Beginners — https://github.com/microsoft/ML-For-Beginners
AI for Beginners — https://github.com/microsoft/AI-For-Beginners
Neural Networks: Zero to Hero — https://github.com/karpathy/nn-zero-to-hero
DL Paper Implementations — https://github.com/labmlai/annotated_deep_learning_paper_implementations
Made With ML — https://github.com/GokuMohandas/Made-With-ML
Hands-On Large Language Models — https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
Advanced RAG Techniques — https://github.com/NirDiamant/RAG_Techniques
AI Agents for Beginners — https://github.com/microsoft/ai-agents-for-beginners
Agents Towards Production — https://github.com/NirDiamant/agents-towards-production
AI Engineering Hub — https://github.com/patchy631/ai-engineering-hub

Step-by-step plan you can follow and show as proof of work

Foundations
1. Learn the basics of machine learning and deep learning
• ML for Beginners, AI for Beginners
Output: 3 small projects with short READMEs that explain the goal, data, and result.

Go deeper
2) Build neural nets from scratch
• Neural Networks: Zero to Hero
Output: a tiny GPT trained on a toy dataset, plus notes on what you changed and why.

Read papers in code
3) Study real architectures by walking through annotated implementations
• DL Paper Implementations
Output: pick one model and re-implement a minimal version. Write what you simplified.

Ship real software
4) Move from notebooks to apps and services
• Made With ML
Output: refactor one project with a simple API, tests, and a one-click run script.

Work with LLMs
5) Learn the core pieces end to end
• Hands-on LLMs
Output: a basic RAG app (retrieval augmented generation) that answers questions on a small knowledge base.

Make RAG better
6) Compare advanced techniques
• Advanced RAG Techniques
Output: run A/B tests on 3 settings and report latency, accuracy, and cost in a table.

Learn agents
7) Build simple agents that take steps toward a goal
• AI Agents for Beginners
Output: an agent that checks a site, writes a summary, and files a ticket.

Take agents toward production
8) Add memory, orchestration, and basic security
• Agents Towards Production
Output: logging, retry logic, and input checks. Note what fails and how you fixed it.

Round out your portfolio
9) Adapt working examples
• AI Engineering Hub
Output: 2 more apps that solve real tasks, each with a clear demo and setup guide.

How to pace this
• One repo per week is a good rhythm.
• Keep a single repo called “ai-engineering-journey” with subfolders per step.
• After each step, post a short write-up with a 30-second screen recording.

What hiring managers look for
• Working code that runs on first try.
• Clear README, data source, and limits.
• Small tests and a simple eval, even if manual.
• Changelog that shows steady progress.

Save this and start with step 1 today.