Module 1: Foundations of AI Engineering
- What is AI engineering? Distinction from data science and ML research
- Core AI concepts: machine learning, deep learning, reinforcement learning
- Software engineering principles applied to AI systems
- Lifecycle of AI projects: data, model, deployment, monitoring
Module 2: Data Engineering for AI
- Data pipelines: ingestion, cleaning, transformation
- Feature engineering and storage systems
- Big data frameworks: Hadoop, Spark, cloud-native solutions
- Case study: building a scalable data pipeline for ML
Module 3: Model Development
- Classical ML algorithms vs. deep learning architectures
- Frameworks: TensorFlow, PyTorch, Scikit-learn
- Experiment tracking and reproducibility (MLflow, Weights & Biases)
- Hands-on: training and evaluating models
Module 4: Deployment and MLOps
- Model serving: REST APIs, gRPC, microservices
- Containerization and orchestration: Docker, Kubernetes
- Continuous integration/continuous deployment (CI/CD) for ML models
- Monitoring and retraining pipelines
- Case study: deploying a recommendation engine
Module 5: AI Systems Architecture
- Designing scalable AI systems
- Edge AI vs. cloud AI
- Multi-agent systems and distributed AI
- Integration with enterprise applications
Module 6: Responsible AI
- Bias detection and mitigation
- Explainability and interpretability (XAI)
- Privacy-preserving AI: federated learning, differential privacy
- Regulatory compliance: GDPR, CCPA, NDPR
Module 7: Advanced Topics
- Generative AI engineering: LLMs, diffusion models
- Reinforcement learning in production systems
- AI for cybersecurity and automation
- AI + IoT integration
Module 8: Future Trends
- AI engineering for autonomous systems
- Quantum AI engineering
- AI agents for self-improving systems
- AI-driven software development (AutoML, code generation)