Now Enrolling · Batch Starts July 12, 2026 · Limited Seats
Advanced RouteProduction AI EngineeringResearch → Production · LLMs, RAG & Agents, GenAIOps
This is not an introductory AI course. A deep, industry-focused specialization for experienced professionals who want to master the architecture, deployment, and orchestration of real-world enterprise-grade AI systems. ⚡
Mentors: Krish Naik & Sourangshu Paul
Starts July 12th, 2026
Sat & Sun · 8–11 PM IST
Duration: 7–8 Months
12
Modules
5
Capstone Projects
25+
Research Papers
2yr
Dashboard Access
7–8
Months Live
▶Transformer Internals & KV Cache▶QLoRA · DPO · GRPO · ORPO▶LangGraph · PydanticAI▶MCP & A2A Protocols▶GraphRAG with Neo4j▶Reasoning Models · DeepSeek-R1▶vLLM · SGLang · llama.cpp▶Mixture of Experts▶Knowledge Distillation & SLMs▶AWS · Kubernetes · CI/CD▶Vision-Language Models▶Speech AI · Whisper Fine-Tuning▶Transformer Internals & KV Cache▶QLoRA · DPO · GRPO · ORPO▶LangGraph · PydanticAI▶MCP & A2A Protocols▶GraphRAG with Neo4j▶Reasoning Models · DeepSeek-R1▶vLLM · SGLang · llama.cpp▶Mixture of Experts▶Knowledge Distillation & SLMs▶AWS · Kubernetes · CI/CD▶Vision-Language Models▶Speech AI · Whisper Fine-Tuning
🎯 Ideal Candidate
Built for Experienced Engineers
This program is for professionals who already know the basics and want to cross into production-grade AI engineering. Not for beginners.
- 🔬ML / Data Engineers2+ years exp, looking to specialize in LLMs and agentic systems
- 💻Software EngineersStrong Python background, pivoting to AI systems architecture
- 🏗️AI Systems ArchitectsWho want to master the end-to-end enterprise AI stack
- 🚀LLM / GenAI DevelopersWho have done RAG basics and want production-grade depth
- 🔐AI Security ProfessionalsFocused on guardrails, RBAC, LLM gateways, and compliance
📋 Prerequisites
What You Need
This is an advanced specialization. You must have a foundation before enrolling.
⚠️ Required Before Joining
- Strong Python Coding Knowledge
- Fundamentals of NLP & Deep Learning
- Fundamentals of Generative AI
- 2+ Years of Professional Experience
- Familiarity with cloud platforms (AWS / GCP / Azure)
- Basic understanding of Docker & REST APIs
👨🏫 Instructors
Learn from Practitioners
Lead Instructor — Senior AI Consultant
Senior AI/LLM Engineer specialized in production agentic systems, fine-tuning pipelines, and enterprise AI deployment. Deep expertise in LangGraph, PydanticAI, MCP & A2A protocols, and multi-agent orchestration frameworks used in Fortune 500 AI teams.
📚 Complete Curriculum
12 Modules. The Full Stack.
From transformer internals to production Kubernetes deployments — every layer of the modern LLM engineering stack, in one program.
Click any module to expand the full topic breakdown. Each module maps to 2–4 weeks of live weekend sessions with hands-on labs.
Transformers 101
- ›Embeddings: From Discrete to Continuous Space
- ›The Attention Mechanism
- ›Self-Attention
- ›Multihead Attention
- ›Masked Multihead Attention
- ›Positional Encoding
- ›Encoder–Decoder Transformers
- ›Encoder-Only Transformers
- ›Decoder-Only Transformers
- ›Cross-Attention
Tokenization Deep Dive
- ›Taxonomy of Tokenization
- ›Word / Subword / Character / Byte level
- ›Byte Pair Encoding
- ›WordPiece
- ›SentencePiece
🏗️ 5 Capstone Projects
Not Exercises. Production Systems.
Every project is a real enterprise-grade system — graded, deployed, portfolio-ready. These are the exact systems enterprise AI teams are hiring for.
01
Medical AI · Fine-Tuning · LLMOps
MedScriptAI
Domain-Specific Medical LLM · Full Post-Training Pipeline
Build a production medical LLM using a complete post-training pipeline on clinical datasets — from synthetic data generation through QLoRA fine-tuning, DPO alignment, multi-adapter vLLM deployment, and full AWS production infrastructure.
- Fine-tune Llama-3.1-8B-Instruct using QLoRA-based SFT on healthcare datasets
- Perform preference alignment with DPO for reasoning, safety & response style
- Generate synthetic instruction data using distilabel
- Deploy multi-adapter inference with vLLM (hot-swappable LoRA)
- Evaluate with ROUGE-L, BERTScore, and LLM-as-a-Judge
- Production API with FastAPI + Docker + AWS ECR + LangSmith tracing
Llama-3.1-8BQLoRADPOTRLUnslothvLLMFastAPIAWS ECRdistilabelLangSmith
02
Knowledge Distillation · Edge Deployment
EdgeReason
Distill a Large Reasoning Model for Efficient Edge Deployment
Compress DeepSeek-R1's reasoning capabilities into Phi-3-mini (3.8B) using custom KL Divergence and Attention Transfer losses from scratch — then quantize to GGUF for CPU-friendly deployment.
- Teacher: DeepSeek-R1-Distill-Qwen-7B → Student: Phi-3-mini-4k-instruct (3.8B)
- Implement KL Divergence + Attention Transfer losses from scratch
- Training on A10G 24GB GPUs tracked with Weights & Biases
- Quantization Pipeline: GGUF format via llama.cpp for CPU deployment
- Inference & Benchmarking with llama-server OpenAI-compatible APIs
DeepSeek-R1Phi-3-miniPyTorchllama.cppGGUFWeights & BiasesTransformers
03
Multimodal RAG · GraphRAG · Enterprise Legal AI
LexisGraph
Enterprise Legal Document Intelligence System
Build an enterprise-grade legal AI using OCR-free document parsing (ColPali), multi-vector Qdrant retrieval, Neo4j knowledge graphs, and Presidio/NeMo security — fully deployed to AWS.
- OCR-Free Parsing: pdf2image + ColPali — no OCR pipelines needed
- Hybrid Retrieval: BM25 + Dense + Reciprocal Rank Fusion (RRF)
- Knowledge Graph Layer: Neo4j for entity relationships
- Adaptive Query Routing across visual, keyword, graph, hybrid paths
- Security: Presidio PII masking + NeMo Guardrails
- RAGAS Evaluation: faithfulness, recall, precision ≥ 0.85 target
- Deployment: AWS + FastAPI + LangChain LCEL + Docker
ColPaliQdrantNeo4jLangChain LCELFastAPIPresidioNeMo GuardrailsRAGAS
04
Multi-Agent · A2A · MCP · Kubernetes
QueryMesh
Production arXiv Research Assistant · Multi-Agent + A2A + MCP
Build a production multi-agent research system with 5 specialized LangGraph agents, FastMCP servers, A2A communication, OpenSearch hybrid retrieval, and full AWS EKS Kubernetes deployment with CI/CD and observability.
- LangGraph supervisor-worker setup with 5 specialized agents + TypedDict state
- All agents expose FastMCP servers with SSE transport + A2A peer delegation
- Hybrid Search: OpenSearch BM25 + Jina AI vector + Reciprocal Rank Fusion
- Upstash Redis cache with SHA256 exact-match — 100×+ faster repeated queries
- Logfire span-level tracing + Langfuse Cloud for token & latency monitoring
- Telegram bot + Gradio web UI for interactive queries
- Production: AWS EKS + Helm + ALB Ingress + GitHub Actions CI/CD
LangGraphFastMCPA2A ProtocolOpenSearchAWS EKSKubernetesLogfireLangfuseRedisJina AI
05
Synthetic Data · Data Engineering · AWS Batch
SynthForge
Large-Scale Synthetic Data Factory for Instruction Dataset Generation
Build the upstream factory that powers MedScriptAI and EdgeReason — a production synthetic data pipeline generating domain-specific instruction datasets at scale using Evol-Instruct, persona-driven prompting, and multi-turn dialogues.
- 1,000+ persona types: clinicians, researchers, students, engineers
- Multi-Turn Conversations: ShareGPT-style dialogues with clarification patterns
- Quality Filtering: HelpSteer2 reward models to retain top-quality samples
- Difficulty Curriculum: embeddings + clustering into easy/medium/hard tiers
- Deduplication: MinHash LSH (0.85 threshold) for duplicate removal
- Dual-Model Validation to reduce mode collapse
- AWS Batch + ECS Fargate with scale-to-zero architecture
- CI/CD: GitHub Actions + CloudWatch + Logfire + wandb + HuggingFace Hub
distilabelArgillaAWS BatchECS FargateHuggingFace HubwandbLogfireFastAPI
⚙️ Core Skills You'll Master
What You Walk Away With
🧠
Transformer Internals
KV Cache, Flash Attention, MHA/MQA/GQA/MLA, RoPE, Scaling Laws from first principles.
Architecture🔧
Advanced Fine-Tuning
LoRA, QLoRA, DoRA, SFT, DPO, GRPO, ORPO — the complete post-training pipeline.
LLM Training📚
Production RAG Systems
Hybrid RAG, GraphRAG, Multimodal RAG, Agentic RAG, Caching, Guardrails & Evaluation.
Retrieval🤖
Multi-Agent Orchestration
LangGraph supervisor-worker patterns, A2A protocol, human-in-the-loop, agent state management.
Agents🔌
MCP & A2A Protocols
Build and deploy MCP servers and A2A-compliant agent systems from scratch. The 2026 enterprise standard.
Protocols📉
Knowledge Distillation
Student-Teacher paradigm, KL Divergence & Attention Transfer losses, GGUF quantization for edge deployment.
Compression👁️
Vision-Language Models
ViT, CLIP, SigLIP, DINOv2, VLM architecture — build multimodal RAG with ColPali.
Multimodal🎙️
Speech AI
Whisper architecture, fine-tuning on custom speech data, building production STT pipelines.
Audio🔀
Mixture of Experts
MoE architecture, load balancing, training and inference tradeoffs vs dense models.
Architecture🧪
Synthetic Data Engineering
Self-Instruct, Evol-Instruct, LLM-as-Judge scoring, deduplication, quality filtering pipelines.
Data🔐
AI Security & RBAC
Guardrails, PII masking, LLM gateways, JWT/SAML-based RBAC, multi-tenancy & data isolation.
Security📡
LLMOps & Observability
LangSmith, Logfire, Langfuse tracing. AWS EKS, Kubernetes, CI/CD, Docker, cost optimization.
Production🛠️ APIs, Frameworks & Tools
The Complete Tech Stack
Every tool you'll work with across the program — categorized by layer.
Agentic Frameworks
LangChainLangGraphPydanticAIFastMCPA2A ProtocolMCP
Fine-Tuning Stack
HuggingFace TRLTransformersPEFTUnslothAxolotlLLaMA-FactorySageMaker
Inference & Serving
vLLMSGLangllama.cppLiteLLMOllamaFastAPIGradio
Vector Databases & Graphs
QdrantOpenSearchFAISSNeo4jChromaUpstash Redis
Observability & Evals
LangSmithLangfuseLogfireWeights & BiasesRAGASInspect AICloudWatch
AI Security & Guardrails
NeMo GuardrailsLlamaFirewallLLM GuardGuardrails AIBedrock GuardrailsPresidio
Cloud & Infrastructure
AWS EKSAWS ECRAWS BatchECS FargateDockerKubernetesHelmGitHub ActionsAirflow
Data & Synthetic Generation
distilabelDataDreamerArgillaHuggingFace HubDoclingColPali
LLM APIs & Models
OpenAI APIAnthropic ClaudeGoogle GeminiJina AIWhisper APIarXiv APIDeepSeek API
✨ Program Features
Everything Included
🎥
Live Weekend Zoom Sessions
Sat & Sun, 8–11 PM IST. Instructor-led, real-time, with live coding and Q&A. Not recorded lectures you watch alone.
Core Delivery🔓
2 Years Dashboard Access
All recordings, notebooks, slides, and updated materials — available for 2 full years after enrollment.
Long-Term Access💬
Dedicated Private Discord
Invite-only community for cohort peers, TA support, job board, research paper drops, and alumni network.
Community🙋
Live Doubt Clearing Sessions
Dedicated sessions for clearing module doubts, debugging capstone projects, and architectural reviews.
Direct Support🏗️
5 Graded Capstone Projects
Production systems, not toy apps. Each project is graded with written feedback — GitHub-ready and interview-ready.
Career Impact📄
25+ Research Paper Breakdowns
Landmark papers decoded in class — DeepSeek-R1, Flash Attention, Scaling Laws, DPO, ColPali, and more.
Research-Grade🗣️
Community Discussion Forum
Structured forum for module Q&A, project sharing, peer code reviews, and collaborative problem solving.
Async Learning🔄
Content Updates
AI moves fast. New modules, updated notebooks, and fresh research drops
Future-Proof🛠️
Private GitHub Codebase
Production-quality code templates, Jupyter notebooks, and starter scaffolding for every module and project.
Hands-On📄 Research Depth
42+ Research Papers, Decoded.
No other program at this price point covers landmark AI research in class. Papers aren't just referenced — they're implemented.
42+
Papers Covered
7–8
Months Live
12
Core Modules
5
Production Projects
▸ AI Foundations
Attention Is All You NeedNeural Machine Translation with Subword Units (BPE)BERT: Pre-training of Deep Bidirectional Transformers▸ KV Cache & Attention Variants
FlashAttention: Fast and Memory-Efficient Exact AttentionFlashAttention-2: Faster Attention with Better ParallelismGQA: Training Generalized Multi-Query Transformer ModelsFast Transformer Decoding: One Write-Head is All You Need (MQA)RoFormer: Enhanced Transformer with Rotary Position Embedding (RoPE)PagedAttention: Efficient Memory Management for LLM Serving (vLLM)Scaling Laws for Neural Language ModelsTraining Compute-Optimal LLMs (Chinchilla)▸ Fine-Tuning & Alignment
LoRA: Low-Rank Adaptation of Large Language ModelsQLoRA: Efficient Finetuning of Quantized LLMsDoRA: Weight-Decomposed Low-Rank AdaptationAdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-TuningInstructGPT: Training Language Models to Follow Instructions (RLHF)DPO: Direct Preference OptimizationORPO: Monolithic Preference Optimization without Reference ModelSelf-Instruct: Aligning LMs with Self-Generated InstructionsBetter & Faster LLMs via Multi-token Prediction▸ Mixture of Experts & Reasoning Models
Mixtral of ExpertsDeepSeek-R1: Incentivizing Reasoning via Reinforcement LearningDeepSeek-V2: Strong MoE LM with Multi-Head Latent Attention (MLA)DeepSeekMath: Pushing Limits of Mathematical Reasoning (GRPO)▸ Knowledge Distillation & SLMs
Distilling the Knowledge in a Neural NetworkDistilBERT: A Distilled Version of BERT▸ Vision Models & VLMs
An Image is Worth 16x16 Words: Vision Transformer (ViT)CLIP: Learning Transferable Visual Models from Natural LanguageSigLIP: Sigmoid Loss for Language Image Pre-TrainingDINOv2: Learning Robust Visual Features without SupervisionColPali: Efficient Document Retrieval with Vision Language Models▸ Speech Models
Whisper: Robust Speech Recognition via Large-Scale Weak Supervision▸ RAG Systems
RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLPSelf-RAG: Learning to Retrieve, Generate, and CritiqueCorrective Retrieval Augmented Generation (CRAG)GraphRAG: From Local to Global Query-Focused SummarizationLLMLingua: Compressing Prompts for Accelerated InferenceSPLADE v2: Sparse Lexical and Expansion Model for IRColBERT: Efficient Passage Search via Contextualized Late Interaction▸ Agents & Production
Chain-of-Thought Prompting Elicits Reasoning in LLMsReAct: Synergizing Reasoning and Acting in Language ModelsFast Inference from Transformers via Speculative Decoding💎 Pricing
Unmatched at This Price
One investment. The complete modern LLM engineering stack. Built for engineers who are serious about 2026.
443
subtopics
Across 12 deep-dive modules
25+
research papers
Decoded & implemented live
5
production systems
Deployed to AWS Kubernetes
60+
tools & frameworks
The complete 2026 stack
⛔ Before This Program
- —Know basic RAG and LangChain
- —Build tutorial-level demos
- —Unfamiliar with production LLMOps
- —Haven't touched A2A or MCP protocols
- —No enterprise-grade projects to show
✅ After This Program
- Fine-tune and align LLMs with QLoRA + DPO
- Deploy multi-agent systems to Kubernetes
- Implement MCP & A2A protocols from scratch
- Own 5 production capstone projects on GitHub
- Speak the language enterprise AI teams hire for
BEST VALUE
Advanced Route Program
Visit checkout page for current pricing 👇
Enroll Now — Secure Your Seat - Live Weekend Zoom Sessions (7–8 months)
- 2 Years Full Dashboard Access
- All Recordings + Notes
- Dedicated GitHub Repository
- Private Discord Community
- 5 Graded Capstone Projects
- 25+ Research Paper Breakdowns
- Community Discussion Forum
- Live Doubt Clearing Sessions
- New Research Paper Drops
- TA / Mentor Support
- Cohort-Based Learning
- Early Access to Updated Content
📅 Batch starts July 12th, 2026
⏰ Sat & Sun · 8 PM – 11 PM IST
⚠️ Limited Seats · For Experienced Professionals Only
📞 Guidance: +91 84848 37781
⏰ Sat & Sun · 8 PM – 11 PM IST
⚠️ Limited Seats · For Experienced Professionals Only
📞 Guidance: +91 84848 37781
❓ FAQ
Common Questions
What are the prerequisites for this program?
Solid Python proficiency and a working understanding of fundamentals of Deep Learning & NLP concepts. No prior LLM or transformer experience is required; the AI Foundations module builds that from scratch.
How long is the course and what is the time commitment?
The program spans 12 core modules across topics from transformer internals to production agent systems. Expect 8–10 hours per week for video lectures, hands-on notebooks, and GitHub repos.
Is this course suitable for beginners in AI?
Not a ground-zero beginner course. You should be comfortable writing Python and know what a neural network does. The curriculum is designed for developers and AI practitioners who want to move from surface-level AI usage to deep, production-grade expertise.
What specific technologies will I master?
LoRA, QLoRA, DPO, GRPO, LangChain, LangGraph, PydanticAI, vLLM, Unsloth, Axolotl, CLIP, Whisper, Zilliz, ColBERT, RAGAS, LangSmith, Logfire, LiteLLM, MCP, A2A, AWS Bedrock, and more — all used in hands-on labs, not just mentioned in slides.
Will I build real-world projects?
Yes. Every major module closes with a working notebook or end-to-end project — a fine-tuned domain LLM, a production multimodal RAG pipeline, a stateful LangGraph agent, and a multi-agent system with MCP and A2A integration.
What kind of infrastructure, pricing, and coding setup is used throughout the course?
The course uses enterprise-style GenAI infrastructure including RunPod, Google Colab, Zilliz Cloud, vLLM, SGLang, and AWS Bedrock. You will build hands-on coding projects across LLMs, RAG, and agents. Total expected infrastructure cost for the full course is around $50–$100.
Does the course cover the latest AI protocols like MCP?
Yes — dedicated modules for both Model Context Protocol (MCP) and Agent-to-Agent (A2A). You build MCP servers and clients from scratch and implement A2A-compliant agent discovery and delegation. These are not surface-level overviews.
Is the curriculum kept up to date?
Actively maintained. DeepSeek-R1, Qwen-3, PageIndex vectorless RAG, and ColBERT late interaction were all added in 2026 as core content. New tools and techniques are integrated as the field moves — enrolled students get all updates.
Is there community support or mentorship available?
Yes — access to a dedicated Discord community and community forum. Questions are typically answered within 24 hours.
How does this course differ from other LLM courses?
Depth and coverage. Most courses stop at RAG or basic agents. This program goes further: reasoning model training, MoE architecture, MCP/A2A protocols, harness engineering, and multimodal pipelines — all with working code.
Enrolling Now · Batch Starts July 12, 2026 · Limited Seats
Stop Building Demos.
Start Shipping Production AI.
Fine-tune LLMs with QLoRA. Deploy multi-agent systems to Kubernetes. Implement MCP & A2A protocols from scratch. This is the program for engineers who are done with tutorials — taught live, every weekend, by Krish Naik & Sourangshu Paul.
For Experienced Professionals · 2+ Years Required · Sat & Sun 8–11 PM IST · July 12, 2026 Start