Whether you are beginning your journey, preparing to enter the job market, or upgrading your technical depth, it provides a clear pathway to move from fundamentals to real engineering competence in the age of AI.
Applied AI Engineering: From Probability to Generative Models & LLM Systems is a 6-month intensive, hands-on program designed to take you from core mathematical and algorithmic foundations to deploying state-of-the-art AI systems, including Generative Models, Transformers, and Large Language Models (LLMs).
Engage in mastering the essentials of linear algebra, calculus, probability, optimization, and neural networks, and then progress to building generative models (VAE, Diffusion), training transformers, and fine-tuning LLMs. Practice mathematical derivations, implement models in NumPy and PyTorch, benchmark performance, and deploy fully functional AI applications. By the end, you’ll be ready to build, optimize, and deploy AI systems, and work as an ML / Generative AI Engineer in the real world.
Core Concepts: Model Hub, Pipelines, Datasets, Tokenizers, Inference APIs, Model Cards, Licensing.
Industry Tools: Hugging Face Transformers, Datasets, Diffusers, Evaluate, Accelerate, PEFT, Gradio.
Hands-on Project: Compare text, vision, speech, and multimodal models by building a multimodal AI demo.
Core Concepts: Dataset management, annotation quality, duplicate detection, train/validation/test splits, embeddings, similarity search.
Industry Tools: FiftyOne, OpenCV, CVAT, Label Studio, COCO Dataset.
Hands-on Project: Clean, organize, and analyze a real-world vision dataset for model training.
Core Concepts: Object Detection, Instance Segmentation, Bounding Boxes, COCO Metrics, IoU, mAP.
Industry Tools: MMDetection, Detectron2, MMCV, MMEngine, COCO API.
Hands-on Project: Fine-tune an object detector and evaluate its performance on a custom dataset.
Core Concepts: Pretrained Models, Transfer Learning, State Dictionaries, Model Freezing, Model Surgery.
Industry Tools: PyTorch, TorchVision, PyTorch Hub, timm, TensorBoard, ONNX Runtime.
Hands-on Project: Compare multiple pretrained CNN backbones and build an optimized image classifier.
Core Concepts: Classification, Regression, Clustering, Reinforcement Learning, Feature Engineering, Model Evaluation.
Industry Tools: Scikit-learn, XGBoost, LightGBM, Optuna, SHAP, Gymnasium, Stable-Baselines3.
Hands-on Project: Build ML models for prediction, clustering, and reinforcement learning using real-world datasets.
Core Concepts: Tensors, Forward & Backward Propagation, Loss Functions, Optimizers, Regularization, Mixed Precision, Overfitting.
Industry Tools: PyTorch, torch.amp, PyTorch Lightning, TensorBoard, Weights & Biases, Optuna.
Hands-on Project: Build a complete deep learning training pipeline with checkpointing and experiment tracking.
Core Concepts: Self-Attention, Multi-Head Attention, Positional Encoding, Encoder-Decoder Models, Fine-tuning, Adapters.
Industry Tools: PyTorch, Hugging Face Transformers, Datasets, Accelerate, PEFT.
Hands-on Project: Fine-tune a Transformer model and compare full fine-tuning with parameter-efficient tuning.
Core Concepts: MLPs, CNNs, RNNs, LSTMs, GRUs, ResNet, U-Net, Autoencoders, Graph Neural Networks.
Industry Tools: PyTorch, TorchVision, timm, PyTorch Geometric, Netron, torch.profiler.
Hands-on Project: Benchmark multiple neural network architectures and recommend the best model for a real-world application.
Core Concepts: Tokenization, Embeddings, Transformer Architecture, Next-Token Prediction, Prompting, Context Windows, Hallucinations.
Industry Tools: Hugging Face Transformers, Tokenizers, Datasets, PyTorch, SentencePiece, Evaluate, Accelerate, bitsandbytes, vLLM.
Hands-on Project: Compare multiple LLMs, evaluate their performance on domain-specific tasks, and analyze inference speed and quality.
Core Concepts: Supervised Fine-Tuning (SFT), Chat Templates, LoRA, QLoRA, Preference Learning, Safety Evaluation.
Industry Tools: Hugging Face Transformers, TRL, PEFT, Datasets, Accelerate, bitsandbytes, Weights & Biases.
Hands-on Project: Fine-tune a chat model using LoRA and evaluate it against the original model.
Core Concepts: Document Chunking, Embeddings, Vector Databases, Hybrid Search, Reranking, Grounded Responses, Citations.
Industry Tools: Sentence Transformers, FAISS, pgvector, Qdrant, BM25, LangChain, LlamaIndex, Ragas.
Hands-on Project: Build an AI document assistant with semantic search, citations, and Retrieval-Augmented Generation.
Core Concepts: Function Calling, AI Agents, Memory, Planning, Multi-Agent Systems, Guardrails, Observability.
Industry Tools: LangChain, LangGraph, LlamaIndex, CrewAI, Microsoft AutoGen, FastAPI, OpenTelemetry.
Hands-on Project: Build an AI agent capable of using external tools, retrieving information, and completing multi-step tasks.
Core Concepts: Feature Extraction, Transfer Learning, Fine-Tuning, Data Augmentation, Domain Adaptation, Model Calibration.
Industry Tools: PyTorch, TorchVision, timm, Albumentations, FiftyOne, CVAT, Label Studio.
Hands-on Project: Fine-tune a pretrained computer vision model for defect detection or image classification.
Core Concepts: Diffusion Process, Latent Diffusion, Prompt Engineering, Guidance Scale, LoRA, Image Generation.
Industry Tools: Hugging Face Diffusers, PEFT, Accelerate, PyTorch, safetensors.
Hands-on Project: Generate high-quality AI images and customize a diffusion model using LoRA.
Core Concepts: Image-to-Image Generation, Inpainting, ControlNet, Edge & Depth Conditioning, Guided Image Synthesis.
Industry Tools: Diffusers, ControlNet, PEFT, Accelerate, OpenCV, FiftyOne.
Hands-on Project: Build an AI-powered image editing and conditional image generation application.
Core Concepts: CLIP Embeddings, Image Captioning, Visual Question Answering (VQA), Multimodal AI, Visual Retrieval.
Industry Tools: Hugging Face Transformers, OpenCLIP, FAISS, Qdrant, FiftyOne, FastAPI, Gradio.
Hands-on Project: Build a multimodal AI assistant capable of understanding images and answering visual questions.
Core Concepts: Quantization, Pruning, Knowledge Distillation, ONNX Export, Edge Optimization, Accuracy vs Performance Trade-offs.
Industry Tools: ONNX Runtime, OpenVINO, TensorRT, ExecuTorch, TensorFlow Lite, PyTorch.
Hands-on Project: Optimize a deep learning model for edge deployment and compare its size, speed, and accuracy.
Core Concepts: REST APIs, Model Serving, Docker, Batch vs Online Inference, Health Checks, Deployment Pipelines.
Industry Tools: FastAPI, BentoML, Docker, MLflow, ONNX Runtime, TorchServe.
Hands-on Project: Deploy an AI model as a REST API using FastAPI and Docker.
Core Concepts: Model Evaluation, Data Drift, Concept Drift, Prediction Logging, Quality Metrics, Feedback Loops.
Industry Tools: Evidently AI, MLflow, WhyLabs, Arize, Great Expectations, Ragas.
Hands-on Project: Build a monitoring dashboard to evaluate deployed AI models and detect performance degradation.
Core Concepts: Incident Response, Model Retraining, CI/CD, Performance Monitoring, Cost Optimization, Model Governance.
Industry Tools: Prometheus, Grafana, OpenTelemetry, MLflow, GitHub Actions, CI/CD Pipelines.
Hands-on Project: Design a production AI monitoring system with automated retraining and operational workflows.
Core Concepts: AI System Design, APIs, Data Pipelines, Background Tasks, Authentication, Caching, Observability.
Industry Tools: FastAPI, Docker Compose, PostgreSQL, pgvector, Redis, Celery, OpenTelemetry.
Hands-on Project: Design and build an end-to-end AI application with scalable architecture and monitoring.
Core Concepts: AI Evaluation, Safety Testing, Bias & Fairness, Privacy, Model Cards, Data Governance, Responsible AI.
Industry Tools: MLflow, Evidently AI, Ragas, Great Expectations, Presidio, Model Cards.
Hands-on Project: Create a comprehensive evaluation pipeline and governance framework for an AI system.
Core Concepts: End-to-End AI Development, Data Preparation, Model Training, Deployment, Documentation, Version Control.
Industry Tools: Git, Docker, FastAPI, MLflow, Gradio, Hugging Face, PyTorch (tool selection based on project).
Hands-on Project: Build a complete industry-ready AI solution by integrating machine learning, NLP, computer vision, or generative AI.
Core Concepts: Technical Presentation, Model Evaluation, Deployment Strategy, Scalability, Documentation, Engineering Best Practices.
Industry Tools: GitHub, Docker, MLflow, Weights & Biases, Architecture Decision Records, Model Cards.
Hands-on Project: Present, defend, and deploy the final capstone project with complete documentation and a production-ready roadmap.
