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Generative AI Mastery Roadmap 2025

Phase 1: Foundations

0–3 months

Build strong fundamentals in AI, math, and programming

Mathematics

  1. 1. Linear Algebra (vectors, matrices, eigenvalues)
  2. 2. Probability & Statistics (distributions, Bayes rule, hypothesis testing)
  3. 3. Calculus (gradients, derivatives, optimization basics)

Programming

  1. 1. Python (NumPy, Pandas, Matplotlib, Scikit-Learn)
  2. 2. Git/GitHub (collaboration & version control)
  3. 3. Basic Linux + Command Line (for ML environments)

Machine Learning Basics

  1. 1. Regression, Classification, Clustering
  2. 2. Overfitting/underfitting, bias-variance tradeoff
  3. 3. Gradient Descent & Optimization

Foundation Projects

  1. 1. Implement Linear Regression & Logistic Regression from scratch
  2. 2. Build a spam classifier using Naive Bayes
  3. 3. Simple recommendation system
Phase 1
Phase 2
Phase 2: Deep Learning Core

3–6 months

Master neural networks, optimization, and deep learning foundations

Neural Networks

  1. 1. Feedforward networks, backpropagation, loss functions
  2. 2. Optimization (Adam, SGD, momentum, LR schedulers)
  3. 3. Regularization (dropout, batch norm, weight decay)

Frameworks

  1. 1. PyTorch (primary industry standard in 2025)
  2. 2. TensorFlow/Keras (secondary)
  3. 3. Deep learning best practices and debugging

Deep Learning Architectures

  1. 1. CNNs (computer vision)
  2. 2. RNNs, LSTMs, GRUs (sequential data)
  3. 3. Attention Mechanisms

Deep Learning Projects

  1. 1. Handwritten digit classifier (MNIST/CIFAR-10)
  2. 2. Image classifier with transfer learning (ResNet, EfficientNet)
  3. 3. Sentiment analysis on IMDB/Yelp reviews
Phase 2
Phase 3
Phase 3: Generative AI Foundations

6–9 months

Learn fundamental generative models & understand how machines create

Generative Models

  1. 1. Autoencoders (AE, VAE)
  2. 2. GANs (Generative Adversarial Networks)
  3. 3. DCGAN, StyleGAN, CycleGAN

Modern Architectures

  1. 1. Diffusion Models (state-of-the-art for images, audio, video)
  2. 2. Transformers (backbone of LLMs & diffusion models)
  3. 3. Understanding latent spaces and generation quality

Implementation Practice

  1. 1. Hugging Face ecosystem and model hub
  2. 2. Papers with Code implementations
  3. 3. Model evaluation and metrics

Generative Projects

  1. 1. Image colorization with Autoencoders
  2. 2. GAN for generating new fashion designs
  3. 3. Text-to-Image Diffusion model (using Hugging Face)
Phase 3
Phase 4
Phase 4: Large Language Models & Multimodal AI

9–15 months

Develop expertise with modern LLMs and multimodal foundation models

Transformers in Depth

  1. 1. Attention, self-attention, multi-head attention
  2. 2. Encoder-decoder architectures
  3. 3. Scaling laws, tokenization (BPE, WordPiece, SentencePiece)

LLM Training & Fine-Tuning

  1. 1. Pretraining vs. Fine-tuning
  2. 2. LoRA (Low-Rank Adaptation), QLoRA
  3. 3. Retrieval-Augmented Generation (RAG)
  4. 4. Prompt Engineering & Prompt Tuning

Advanced Training Techniques

  1. 1. Instruction tuning & alignment (RLHF, DPO, Constitutional AI)
  2. 2. Model evaluation and safety considerations
  3. 3. Distributed training and optimization

Multimodal Models

  1. 1. CLIP (text+image alignment)
  2. 2. BLIP, Flamingo, LLaVA, Gemini-style architectures
  3. 3. Audio (Whisper, SpeechT5, MusicGen)
  4. 4. Video generation (Pika, Sora-style diffusion transformers)

LLM Projects

  1. 1. Train a small GPT-like model on custom dataset
  2. 2. Build a chatbot with RAG + vector databases (FAISS, Pinecone, Weaviate)
  3. 3. Fine-tune LLaMA-2/3 or Mistral for domain-specific tasks
  4. 4. Image caption generator (CLIP + LLM)
  5. 5. Voice-to-text assistant (Whisper + GPT + TTS)
Phase 4
Phase 5
Phase 5: Systems, Deployment & Scaling

15–20 months

Move from building models to building products & systems

MLOps for Generative AI

  1. 1. Experiment tracking (Weights & Biases, MLflow)
  2. 2. Model versioning (DVC, Hugging Face Hub)
  3. 3. CI/CD pipelines for ML
  4. 4. Model monitoring (latency, drift, hallucination tracking)

Serving & Deployment

  1. 1. REST & gRPC APIs (FastAPI, Flask)
  2. 2. Model quantization & optimization (ONNX, TensorRT, vLLM)
  3. 3. Serverless + cloud (AWS, GCP, Azure)
  4. 4. Edge deployment (mobile & IoT inference)

Scalable Infrastructure

  1. 1. Kubernetes, Docker, Ray, Spark
  2. 2. Vector databases for RAG (Weaviate, Pinecone, Milvus)
  3. 3. GPU acceleration (CUDA, Triton inference server)
  4. 4. Load balancing and auto-scaling

Production Projects

  1. 1. Deploy a fine-tuned chatbot as SaaS (with FastAPI + Docker + Streamlit)
  2. 2. Create an AI writing assistant (RAG + LLaMA + Pinecone)
  3. 3. Build scalable API for image generation (Stable Diffusion inference)
  4. 4. Implement AI video generator pipeline (diffusion + transformer)
Phase 5
Phase 6
Phase 6: Industry Readiness & Advanced Topics

20–24 months

Become a full-stack Generative AI Engineer ready for top companies or startups

Advanced Model Research

  1. 1. Efficient Transformers (FlashAttention, MoE, RWKV)
  2. 2. Reinforcement Learning with LLMs (PPO, DPO)
  3. 3. AI safety & alignment (hallucinations, bias, interpretability)
  4. 4. Evaluation metrics for LLMs & diffusion models

Specialization Tracks

  1. 1. NLP/LLMs: Custom GPT-like assistants
  2. 2. Vision & Diffusion: Image/video generation at scale
  3. 3. Audio & Speech: AI voices, music generation
  4. 4. Multimodal Agents: AI that sees, hears, and talks

Industry Tools

  1. 1. Hugging Face, LangChain, LlamaIndex
  2. 2. OpenAI/Anthropic API integrations
  3. 3. Vector DBs + RAG at scale
  4. 4. Production monitoring and optimization

Capstone Projects

  1. 1. Build your own domain-specific GPT (healthcare, law, finance)
  2. 2. AI-powered video generator with captions + voiceover
  3. 3. End-to-end multimodal assistant (speech → text → image → video)
  4. 4. Open-source contribution (implementing a new LLM feature/model)
Phase 6
Phase 7
Phase 7: Portfolio & Career

Parallel Throughout

Prove expertise & land opportunities

Portfolio Development

  1. 1. GitHub projects (clean repos, notebooks, demos)
  2. 2. Write blogs/papers on Medium, Substack, arXiv (if research-oriented)
  3. 3. Contribute to open-source (Hugging Face, LangChain, Stable Diffusion repos)
  4. 4. Create 2–3 real-world deployable apps (SaaS-style)

Professional Presence

  1. 1. Build a personal website showcasing Generative AI Portfolio
  2. 2. Technical writing and thought leadership
  3. 3. Conference presentations and networking
  4. 4. Industry mentorship and community involvement

Career Preparation

  1. 1. System design for AI systems interview prep
  2. 2. Technical interview practice for AI roles
  3. 3. Understanding of AI ethics and responsible AI practices
  4. 4. Business acumen and product thinking for AI applications

🎉 Congratulations! You're a Generative AI Expert!

You've mastered the complete Generative AI stack and are ready to build cutting-edge AI applications and systems.