Module 1: Foundations of AI Software Development
- What is AI software development?
- Differences between traditional software engineering and AI-driven systems
- Core AI concepts: machine learning, deep learning, NLP, computer vision
- Lifecycle of AI projects: data → model → deployment → monitoring
Module 2: Programming for AI
- Python as the primary language for AI development
- Libraries: NumPy, Pandas, Matplotlib, Scikit-learn
- Introduction to TensorFlow and PyTorch
- Hands-on: building a simple ML model
Module 3: Data Engineering
- Data collection, cleaning, and preprocessing
- Feature engineering and selection
- Big data frameworks: Spark, Hadoop
- Case study: preparing a dataset for predictive modeling
Module 4: Model Development
- Classical ML algorithms: regression, classification, clustering
- Deep learning architectures: CNNs, RNNs, transformers
- Experiment tracking and reproducibility (MLflow, Weights & Biases)
- Hands-on: training and evaluating models
Module 5: Deployment and MLOps
- Model serving: REST APIs, gRPC, microservices
- Containerization and orchestration: Docker, Kubernetes
- CI/CD pipelines for AI models
- Monitoring drift and retraining pipelines
- Case study: deploying a recommendation engine
Module 6: AI Systems Architecture
- Designing scalable AI systems
- Edge AI vs. cloud AI
- Multi-agent systems and distributed AI
- Integration with enterprise applications
Module 7: Responsible AI
- Bias detection and mitigation
- Explainability and interpretability (XAI)
- Privacy-preserving AI: federated learning, differential privacy
- Regulatory compliance: GDPR, CCPA, NDPR
Module 8: Advanced Topics
- Generative AI: LLMs, diffusion models
- Reinforcement learning in production systems
- AI for cybersecurity and workflow automation
- AI + IoT integration
Module 9: Future Trends
- AI engineering for autonomous systems
- Quantum AI software development
- AI agents for self-improving systems
- AI-driven software development (AutoML, code generation)