⚙️ Machine Learning Systems
Build production-ready ML systems from data pipelines to deployment. Learn distributed training, model serving, MLOps monitoring, and A/B testing for machine learning at scale.
Your Learning Journey
1. Foundations
Core concepts and design principles for ML systems
2. ML Fundamentals
Machine learning basics and algorithms
3. Data & Features
Build robust data pipelines and engineer features
4. Data Processing
Large-scale data processing and ETL
5. Model Development
Advanced training strategies and serving architectures
6. Model Optimization
Performance tuning and efficiency improvements
7. Production ML
MLOps, monitoring, and experimentation
8. Industry Applications
Real-world ML applications across industries
9. Advanced Topics
Cutting-edge research and emerging ML paradigms
Neuromorphic Computing
Quantum ML
Advanced AI Agents
Advanced Reasoning Systems
Neural Architecture Search (NAS)
Federated Learning Systems
Mixture of Experts (MoE)
Advanced Distributed Training
Model Compression Techniques
Real-time ML Inference Systems
Computer Vision Systems Design
Advanced Recommender Systems Architecture
NLP Systems Architecture & Scaling
Advanced Search Systems Architecture
Serverless MLOps Architecture
10. Evaluation
Additional evaluation topics
11. Model Interpretation
Additional model-interpretation topics
12. Edge Computing
Additional edge-computing topics
13. Advanced Architectures
Additional advanced-architectures topics
14. Real Time Systems
Additional real-time-systems topics
15. Ai Safety
Additional ai-safety topics
16. Platform Architecture
Additional platform-architecture topics
17. Ml Engineering
Additional ml-engineering topics
🚀 New to AI? Start with GenAI!
Most modern AI applications use Generative AI. Learn LLMs, RAG, and AI agents before diving into traditional ML infrastructure.