GenAI-Architect-70-Hands-On-Projects

GenAI-Architect-Skill-Upgrade-Hands-On-Projects

📖 GenAI Architect Academy – 70+ Hands-On Projects for Skill Upgrade from Zero to Production

📚 GenAI Architect: From Zero to Production 🚀

A comprehensive, hands-on video series transforming you into a Generative AI Architect through 70 structured videos. Perfect for beginners and pros alike!

🚀 Course Overview

🔥 What Makes This Unique?

📑 Phase 1: Foundations 🏗️ (Videos 1-15)

Build AI/ML basics from scratch with your first generative project.

# 🎬 Title 🔑 Key Concepts 🛠️ Tools ⚡ Hands-On
1 🎤 Introduction to AI and GenAI AI history, supervised/unsupervised - Install Python, explore demos
2 📊 Machine Learning Basics Data splits, regression, classification Scikit-learn Linear regression model
3 🧠 Neural Networks Fundamentals Neurons, layers, activation - Simulate neurons in Python
4 🏗️ Deep Learning Essentials CNNs, RNNs, optimizers TensorFlow/Keras MNIST NN trainer
5 📈 Introduction to Generative Models Distributions, sampling - Random data generation
6 🎨 Autoencoders and VAEs Encoder/decoder, KL divergence PyTorch VAE image generator
7 ⚡ GANs Basics Generator/discriminator PyTorch Simple digit GAN
8 🛠️ Data Handling for GenAI Preprocessing, augmentation Pandas, NumPy NLP dataset prep
9 🐍 Python for GenAI Libraries, environments NumPy, Pandas Data visualization
10 🚀 Mini-Project: Image Generator with VAEs Latent space exploration PyTorch, Colab Train & generate new images
11 ⚖️ Ethics in AI Bias, fairness metrics - Bias analysis
12 💻 Hardware for GenAI CPUs, GPUs, TPUs - Compare CPU/GPU in Colab
13 ☁️ Cloud Platforms for Beginners - Google Colab Deploy scripts
14 📏 Evaluation Metrics FID, BLEU Custom Python Evaluate GAN
15 🎯 Capstone: Basic GAN for Custom Data Iteration on failures PyTorch Custom image generator

📖 Detailed Guide to Phase 1 Concepts 📚

This section provides world-class, comprehensive yet accessible explanations of Phase 1 courses with simple language blended with technical precision, ensuring beginners grasp fundamentals while professionals appreciate depth.

These detailed explanations ensure comprehensive understanding while maintaining accessibility. Each concept integrates hands-on practice in labs for mastery.

📑 Phase 2: Core GenAI Concepts 🤖 (Videos 16-35)

Dive into LLMs, transformers, RAG, and multimodal with practical projects.

# 🎬 Title 🔑 Key Concepts 🛠️ Tools ⚡ Hands-On
16 📝 NLP Basics: Text & Embeddings Tokenization, Word2Vec NLTK, Gensim Sentence similarities
17 🔄 Sequence Models RNNs, LSTMs, GRUs Keras LSTM text predictor
18 ⚡ Transformers Self-attention, multi-head PyTorch Simple attention layer
19 🧠 BERT & Pre-trained Models Bidirectional, fine-tuning Hugging Face BERT sentiment analysis
20 🎤 GPT Evolution GPT-1 to GPT-4o, scaling OpenAI API Text generation with GPT-2
21 🚀 Mini-Project: Chatbot with GPT-2 Conversation optimization Hugging Face Fine-tune on dialogues
22 🎨 Diffusion Models Stable Diffusion basics Diffusers library Image generation
23 🔗 Multimodal GenAI CLIP, text-image alignment OpenAI CLIP Image classification
24 🔊 Audio Generation WaveNet, Tacotron TensorFlow Simple audio synthesis
25 🎥 Video Generation Frame prediction, GANs PyTorch Video Generate short clips
26 🎛️ Fine-Tuning LLMs PEFT, LoRA Hugging Face PEFT Fine-tune LLaMA
27 📊 Datasets Hub Quality curation Datasets library Load & preprocess data
28 🚀 Mini-Project: Stable Diffusion Custom Styling, conditioning Diffusers Generate styled images
29 💡 Prompt Engineering Chain-of-thought, few-shot OpenAI Playground Optimize complex prompts
30 📈 LLM Evaluation Benchmarks, perplexity EleutherAI harness Model benchmarking
31 🔍 RAG Fundamentals Vector search, retrieval FAISS, LangChain Simple RAG pipeline
32 🗄️ Vector Databases Pinecone, indexing HNSW Embeddings storage
33 🚀 Mini-Project: RAG Q&A System Retrieval integration LangChain, Hugging Face Query knowledge base
34 🛡️ Hallucination Mitigation Grounding, confidence - Detect & correct hallucinations
35 🎯 Capstone: Multimodal Chatbot with RAG Integration patterns PyTorch, LangChain Deploy via Streamlit

📑 Phase 3: Advanced Techniques ⚡ (Videos 36-50)

Master scaling, optimization, and production workflows.

# 🎬 Title 🔑 Key Concepts 🛠️ Tools ⚡ Hands-On
36 ⚡ Quantization & Pruning Efficiency trade-offs Torch Quantize Quantize an LLM
37 🔗 Distributed Training DP, MP, DDP Hugging Face Accelerate Multi-GPU training
38 🤖 Agentic AI ReAct, tool calling LangGraph Web search agent
39 🧠 RLHF PPO, reward models TRL library Fine-tune with feedback
40 🚀 Mini-Project: Task Automation Agent Memory management LangChain Email summarization
41 🔍 Advanced Multimodal VLMs, fusion layers Hugging Face Image captioning
42 💻 Code LLMs GitHub Copilot CodeLlama Code generation
43 🔒 Security in GenAI Adversarial attacks - Test LLM defenses
44 💰 Cost Optimization Token caching, batching OpenAI monitoring Cost optimization
45 🚀 Mini-Project: Scalable RAG with Agents Async processing LangChain, FAISS Research assistant
46 🔬 Emerging Trends Mixture of Experts, o1 - Implement simple MoE
47 🔒 Federated Learning Privacy preservation Flower Federated fine-tuning
48 📊 Benchmarking Latency, throughput Torch Profiler Profile pipeline
49 🌱 Sustainability Carbon footprint CodeCarbon Measure emissions
50 🎯 Capstone: Advanced Multimodal Agent Modular design PyTorch, LangChain Deploy to Spaces

📑 Phase 4: Architect-Level Mastery 🏢 (Videos 51-70)

Design, build, and deploy production-grade GenAI systems.

# 🎬 Title 🔑 Key Concepts 🛠️ Tools ⚡ Hands-On
51 🏗️ System Architecture Microservices, patterns Draw.io Sketch RAG system
52 🐳 Deployment Docker, Kubernetes Minikube Containerize LLM
53 🔌 API Design REST, rate limiting FastAPI Inference API
54 📈 Monitoring & Logging Prometheus, alerts ELK stack Model logging
55 🚀 Mini-Project: Cloud RAG API CI/CD deployment AWS/Heroku Host free tier
56 🔀 Hybrid Systems Ensemble ML models Scikit-learn + LLMs Hybrid classifier
57 📋 Case Studies Healthcare, finance compliance HIPAA analysis Propose system
58 ⚖️ Scaling Architecture Load balancing, Redis Sharding Implement caching
59 🧪 A/B Testing Statistical evaluation - Test prompts
60 🚀 Mini-Project: Enterprise LLM System User auth, scaling FastAPI, Docker Simulate production
61 ⚖️ Ethical Auditing Bias, explainability SHAP Audit model
62 ☁️ Serverless GenAI Lambda, event-driven AWS Lambda Serverless inference
63 🛡️ Fault Tolerance Redundancy, retries Circuit breakers Resilient pipeline
64 👥 Team Collaboration MLflow version control DVC Track experiments
65 🚀 Mini-Project: Production Pipeline Vision/text orchestration Kubernetes Multi-modal deployment
66 🔮 Future-Proof Design Modular plugins - Upgradable agent
67 📜 Regulatory Compliance GDPR, AI Acts - Privacy handling
68 ⚙️ Hardware Optimization TPUs, ASICs Google Cloud TPUs TPU training
69 🎙️ Leadership in GenAI Business alignment - GenAI project pitch
70 🎯 Capstone: Full GenAI Platform End-to-end architecture FastAPI, Docker, AWS Portfolio piece

🎯 Prerequisites & Learning Path

🏆 Career Impact

Career Path Skills Gained Target Companies
AI Engineer Model training, deployment Tech startups, FAANG
ML Architect System design, scaling Google, OpenAI, Meta
Data Scientist Advanced GenAI, research Netflix, Tesla
Product Manager AI strategy, ethics Airbnb, Spotify
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