Summary
Key Takeaways
- Modern AI evolved from NN → CNN → RNN → Transformer
- Supervised, unsupervised, reinforcement learning and their roles
- Importance of data pipelines and GPU acceleration
- How training → inference pipelines are built in real systems
- Vector DBs and RAG for knowledge-augmented AI
- CNCF MLOps ecosystem: Kubeflow, Spark, MLflow, Ray, etc.
- NVIDIA acceleration stack for training & inference
What You Should Understand Now
- AI fundamentals and building blocks
- Architecture patterns for training & inference
- Core tools and ecosystem components
- Practical directions for self-paced learning
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