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

• 🎯 70 Videos across 4 progressive phases
• 🛠️ Hands-on Focus: Code-along, projects, and capstones
• 🏗️ Architect Emphasis: System design, production, ethics
• 🌐 Industry-Ready: Mirrors OpenAI, Google, and enterprise standards
• 🤝 Community-Driven: Share projects in comments!

🔥 What Makes This Unique?

• ✅ ** Prerequisites**: Any Programming Languages - Example: Python / R / Rust / Go / Java
• 📈 Progressive Difficulty: Easy → Advanced → Master
• 🎬 Video Style: 10-20min theory + 30-60min projects
• 🆓 Free Tools: Google Colab, Hugging Face, PyTorch
• 📊 Real-World Pacing: 1-2 videos/day, ~75 days total
• 🔄 Emerging Trends: o1 reasoning, multimodal, 2025 tech

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📑 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

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📖 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.

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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

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📑 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

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📑 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

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🎯 Prerequisites & Learning Path

• 🎓 **Better to have Good Coding Knowledge in Anyone Programming Language: Starts with any one - Ex: Phython
• 📅 Time Commitment: 75 days (flexible pacing)
• 🛠️ Free Resources: All tools covered
• 📈 Skill Levels: Beginner → Intermediate → Advanced → Expert
• 🤝 Community Support: Engage via comments and forums

🏆 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

Here’s a high-level introduction to why the course syllabus in GenAI‑Architect‑70‑Hands‑On‑Projects is extremely useful for someone aiming for a GenAI Architect role — and how it helps you not just get the job, but perform in real-world settings. I’ll call out the “why” in a bold way, keep it conversational, a dash of tradition (because yes, fundamentals still matter), and a bit skeptical flair (because you should always ask: “why am I doing this?”).

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Why This Course Curriculum Matters for a GenAI Architect Role ?

1. End-to-end scope, not just theory Too many trainings stop at design or theory. This one (based on the “70 hands-on projects” in the title) suggests you’ll dive into full workflows — from conceptualisation to deployment. That aligns exactly with what real GenAI architects do: you don’t just design a model, you architect a solution (data, model, infrastructure, integration, monitoring).

2. Real-world scenario readiness Good architects don’t work in toy land. This curriculum’s “hands-on projects” framework means you’ll practise in spaces that mimic real systems: enterprise intelligence, production pipelines, scale, operationalisation. When an interviewer asks “tell us about how you built and deployed a GenAI service”, you’ll have stories — not just “I trained a model on Kaggle”.

3. Bridging the gap of job-readiness The GenAI Architect role isn’t just about ML research, it’s about system architecture, stakeholder alignment, cost controls, performance trade-offs, tooling, infrastructure. This course hits that blend: technical + architectural + operational. That’s rare and therefore valuable.

4. Hands-on = demonstrable portfolio Interviewers love to see “here’s what I built” rather than “here’s what I read”. With 70 projects (yes, seventy!) you’ll build a portfolio. You can show up with Git repos, case studies, architecture docs, maybe even live demos. That gives you credibility.

5. Frameworks + tools + methodology A GenAI architect must know modelling (LLMs, embeddings, prompt engineering), systems (APIs, serving, orchestration), infrastructure (cloud, containers, monitoring), data (ingestion, cleaning, governance). This syllabus appears broad enough to cover most of these. That breadth matters: you’ll need to speak fluent “data-to-deployment”.

6. Prepared for real constraints In real life you’ll face latency concerns, cost budgets, scalability, maintainability, governance, ethics, versioning. Fancy “train a huge model” stuff is fun but often impractical. A curriculum with projects likely faces those constraints — making you adapt, design trade-offs, cost-optimize. That’s what hiring managers want.

7. Traditional fundamentals + modern GenAI twist As you prefer the “how things have always been done” vibe: strong architecture discipline, design patterns, modularity, documentation. Then layered on top: GenAI methods (LLMs, prompt tuning, embeddings, retrieval augmented generation). This curriculum gives you both — the “old school” architecture discipline + “new school” AI toolkit.

8. Confidence for leadership / presales / stakeholder talk GenAI Architects often operate at the intersection of business, tech, and product. They have to translate business needs (“we need automation in customer service”) into architecture (“we will build … using LLM, vector DB, API, microservices…”). With many hands-on projects you’ll practise not just coding but articulating architecture, trade-offs, ROI. That helps you sell solutions, not just build them.

9. Interview readiness When you go into interviews for roles like “Lead GenAI Architect”, “Principal AI Architect”, “Solution Architect – GenAI”, you’ll get asked scenario questions: “We have 100M documents, how do we build a retrieval-augmented system?”, “How would you optimise cost for inference at scale?”, “How do you version and monitor LLM deployments?”. With this curriculum you’ll have done similar work. You can answer with confidence.

10. Scalability + future-proofing GenAI is moving fast. The architecture you learn today needs to flex tomorrow. If you get exposure through 70 diverse projects, you’re less rigid, more adaptable. Instead of “I only know this one model”, you’ll know “I know how to design systems that swap in whatever model or pipeline tomorrow”. That side of readiness keeps you relevant.

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How to get maximum value from this course (because the curriculum is good — but you must show up and engage)

• Treat each project as real: build architecture diagrams, write README as if you’re handing to another engineer.
• Document your decisions (fits your style — you like traditional documentation). Why you did X, why you picked Y. That’s gold in interviews.
• Keep a portfolio site (even a simple GitHub Pages) with projects linked, outcomes, challenges faced.
• After each project ask: “what would I do in production? What’s the cost? What’s the latency? How do I monitor failure modes?”
• Connect to business use-cases: “why is this model choice helping the business? What’s the ROI?”
• Reflect on architecture: data flow, modules, APIs, infra, model, retraining, scaling. Use diagrams.
• Share your work: blog, LinkedIn posts — shows initiative, passion.
• Stay skeptical: every project should ask questions like “what happens when the model drifts?”, “who owns data?”, “how secure is this?”, “what’s the fallback when AI fails?” — because real architecture needs that mindset.

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Summary:

This course isn’t just useful, it could be game-changing for your GenAI Architect journey

— if you engage seriously. It gives you exactly the breadth (architecture + AI) and depth (hands-on) that hiring managers and real-world scenarios demand. -So yes — it’s exactly aligned with your goal of “Snowflake + AWS + GenAI + architecture” kind of roles.

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