--- source_url: "https://advancedroute.krishnaik.cloud/?utm_source=openai" title: "Advanced Route — Production AI Engineering | Krish Naik Academy" mirrored_at: 2026-08-15T03:41:55.482Z host: advancedroute.krishnaik.cloud cited_in_42a: true mirror_canonical: "https://index.42a.ai/advancedroute.krishnaik.cloud/index__q__utm_source_openai" --- > **Original source:** https://advancedroute.krishnaik.cloud/?utm_source=openai - [Syllabus](#syllabus) - [Projects](#projects) - [Skills](#skills) - [Tech Stack](#techstack) - [Pricing](#pricing) - [FAQ](#faq) [Enroll Now](https://learn.krishnaikacademy.com/web/checkout/6a16f3791a026c570b1372d9) Now Enrolling · Batch Starts July 19, 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 19th, 2026** **Sat & Sun · 8–11 PM IST** Duration: **7–8 Months** 16 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 Engineers**2+ years exp, looking to specialize in LLMs and agentic systems - 💻 **Software Engineers**Strong Python background, pivoting to AI systems architecture - 🏗️ **AI Systems Architects**Who want to master the end-to-end enterprise AI stack - 🚀 **LLM / GenAI Developers**Who have done RAG basics and want production-grade depth - 🔐 **AI Security Professionals**Focused 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 - Experience building Python projects - Familiarity with cloud platforms (AWS / GCP / Azure) - Basic understanding of Docker & REST APIs 👨‍🏫 Instructors ## Learn from Practitioners ![Krish Naik](https://advancedroute.krishnaik.cloud/assets/krishnaik-Du6MRIch.jpeg) Mentor & Founder Krish Naik One of India's leading AI educators with 1M+ YouTube subscribers. Founder of iNeuron and Krish Naik Academy. Author of multiple AI courses covering production ML, GenAI, and LLMOps. Known for bridging academic research with real-world deployment at scale. ![Sourangshu Paul](https://advancedroute.krishnaik.cloud/assets/sourangshu-BtpqvUoW.jpeg) Lead Instructor — Senior AI Consultant Sourangshu Paul 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. 🤝 Teaching Assistants ## Support Throughout Your Journey ![Divesh Jadhwani](https://advancedroute.krishnaik.cloud/assets/divesh-Td2ResZq.jpeg) Teaching Assistant — AI Engineer Divesh Jadhwani AI Engineer and Industrial AI Trainer with 3+ years of teaching and technical mentoring across academia and enterprise. Specialized in Generative AI, Agentic Systems, Deep Learning, and Enterprise AI deployment. ![Yash Patil](https://advancedroute.krishnaik.cloud/assets/yash-CaRPJweD.jpeg) Teaching Assistant — AI Engineer Yash Patil AI Engineer with experience building production-grade RAG systems, agentic LLM applications, and knowledge graph-based retrieval architectures using LangGraph, LlamaIndex, Mem0, and FastAPI. Skilled in evaluation-driven AI development (DeepEval, RAGAS) and AI governance frameworks for autonomous agent systems. 📚 Complete Curriculum ## 16 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 LangChainLangGraphPydanticAILlamaIndexFastMCPA2A Protocol Fine-Tuning Stack HuggingFace TRLTransformersPEFTUnslothAxolotlLLaMA-FactorySageMaker Inference & Serving vLLMSGLangllama.cppLiteLLMOllamaFastAPIGradio Vector Databases & Graphs QdrantOpenSearchFAISSNeo4jChromaUpstash RedisMilvusWeaviate 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 HubDoclingColPaliLlamaParseLiteParse 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 ## 25+ Research Papers, Decoded. No other program at this price point covers landmark AI research in class. Papers aren't just referenced — they're implemented. 25+ Papers Covered 7–8 Months Live 16 Core Modules 5 Production Projects 💎 Pricing ## Unmatched at This Price One investment. The complete modern LLM engineering stack. Built for engineers who are serious about 2026. 200+ hours of content Live Zoom sessions across 16 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](https://learn.krishnaikacademy.com/web/checkout/6a16f3791a026c570b1372d9) - 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 19th, 2026** ⏰ Sat & Sun · 8 PM – 11 PM IST ⚠️ Limited Seats · For Experienced Professionals Only 📞 Guidance: [+91 84848 37781](tel:+918484837781) ❓ 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 16 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 19, 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. [Enroll Now](https://learn.krishnaikacademy.com/web/checkout/6a16f3791a026c570b1372d9) [Talk to Counsellor](tel:+918484837781) For Experienced Professionals · 2+ Years Required · Sat & Sun 8–11 PM IST · July 19, 2026 Start