AI Engineering From Scratch to Production

Foundation concepts and programming skills for AI engineering:

• Introduction to AI and Machine Learning
• Python programming for AI
• NumPy and Pandas for data manipulation
• Mathematical foundations
• AI ethics and responsible development

Core ML concepts and engineering practices:

• Supervised and unsupervised learning
• Model selection and evaluation
• Feature engineering
• Model validation techniques
• ML pipeline design

Deep learning architectures and implementation:

• Neural network fundamentals
• TensorFlow and PyTorch
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer learning

Understanding and working with modern LLMs:

• Transformer architecture
• Fine-tuning pre-trained models
• Prompt engineering
• GPT, BERT, and other LLMs
• LLM deployment strategies

Building intelligent autonomous agents:

• Agent architectures
• Multi-agent systems
• Reinforcement learning
• Tool integration
• Agent orchestration

Production-ready ML systems and operations:

• MLOps principles
• Model versioning
• CI/CD for ML
• Model monitoring
• A/B testing for models

Deploying AI systems at scale:

• AWS/Azure/GCP AI services
• Containerization with Docker
• Kubernetes for ML
• Serverless AI deployment
• API development

Specialized AI application domains:

• Computer vision projects
• Natural language processing
• Speech recognition
• Generative AI applications
• Multimodal AI systems

Enterprise-grade AI system design:

• Scalable AI architectures
• AI security best practices
• Data privacy and compliance
• Performance optimization
• Cost management