50+ live sessions every year across 6 major tracks. Join anytime. Attend any session. Build 25+ production AI systems. All replays accessible during active subscription. Curriculum evolves continuously as AI evolves.
Understand the platform, learning approach, and how to maximize your ROI from live sessions.
Build an intelligent healthcare triage system. AI-powered patient assessment → production deployment.
Reduce API costs by 80%. Implement intelligent caching with Redis for LLM responses.
Beyond vector databases. Build high-performance RAG without embeddings using advanced indexing.
Leverage Google's ADK to build production-ready autonomous agents. Tool use → deployment.
Deep dive into agent design patterns. Compare LangGraph, CrewAI, AutoGen, and custom frameworks.
Build stateful agents that remember context. Implement conversation memory and intelligent caching.
Integrate N8n workflows with Model Context Protocol. Build interactive Streamlit dashboards.
Your annual subscription gives you access to all live sessions, code repositories, and other resources that will be shared in the session. We run minimum one session every weekend — Sessions will happen from any track. Sessions are selected based on the current industry need and market trends.
Whether the topic is Agents, RAG, or MLOps — every session is designed the same way. You start as a beginner, leave as someone who deployed something to production.
We're not another video course platform. Here's exactly how our live, production-focused curriculum differs from what you get on traditional online learning platforms.
| Feature | Udemy | Coursera | Udacity | TMLC Academy Live |
|---|---|---|---|---|
| Content Format | Pre-recorded videos Watch at 2x speed, pause, repeat. Same videos for everyone. | Pre-recorded lectures University-style video lectures. Fixed content, no interaction. | Pre-recorded + projects Videos + pre-defined projects. No live instruction. | Live 2-hour sessions Real-time coding. Ask questions. See decisions made live. New every week. |
| Production Deployment | Rare/Optional Most courses end with Jupyter notebooks. Production is an "extra" if covered at all. | Not emphasized Academic focus. Theory-heavy. Deployment is brief or skipped. | Simulated environments Deploy to Udacity's sandbox. Not real AWS/K8s/production infra. | Every session ends with deployment AWS Lambda, K8s, ECS, Render. Real infrastructure. Production-grade systems. |
| Systems You Build | Demo/toy projects Iris classification, MNIST digits, Titanic survival. Not portfolio-worthy. | Academic exercises Assignments for grades. Not designed for real-world interviews. | Capstone projects 1-2 larger projects. Still classroom examples, not production systems. | 25+ live production projects HIPAA agents, Kafka pipelines, multi-agent systems, RAG at scale. Built live, deployed to production. |
| Curriculum Updates | Static/rarely updated Videos from 2020-2022. Outdated by the time you take them. No live updates. | Semester-based updates University model. Curriculum updated once a year at best. | Periodic refreshes Update content every 6-12 months. Still lags behind AI's weekly evolution. | New session every weekend Curriculum evolves weekly. Cover new models, techniques, tools as they drop. |
| Instructor Interaction | Forum Q&A only Post questions, wait days for generic responses. No live interaction. | Discussion forums TAs answer questions. Never talk to actual instructors. | Code reviews (extra cost) Pay extra for mentor reviews. Asynchronous, not live. | Live Q&A every session Ask questions in real-time. Watch global AI practitioners code and debug live. |
| Architecture Thinking | Not taught Learn tools, not when/why to use them. No system design discussions. | Theory only Learn patterns in slides. Don't see trade-offs made in real systems. | Some guidance Project rubrics show "good" architecture. Don't learn the why behind decisions. | Every session, live RAG vs fine-tuning? Agents vs workflows? See real decisions debated and made. |
| Tech Stack Coverage | Fragmented One course teaches LangChain, another Pinecone. Piece together yourself. | Academic tools Focus on concepts, not production tools. Sklearn, not LangGraph. | Industry tools Cover modern stack. But still pre-recorded, no live troubleshooting. | Full production stack LangChain, LangGraph, Pinecone, Weaviate, Kafka, K8s, AWS — all integrated, all live. |
| Time to Portfolio | 6-12 months Take 5-10 courses. Cobble projects together. Still no production deployments. | 6-9 months Complete specialization. Get certificate. Portfolio still weak for senior roles. | 4-6 months Faster than others. But projects are still "nanodegree projects," not real systems. | 1st month itself Build 2 production systems in your first month. 25+ systems by end of year. Portfolio starts immediately. |
| Community Quality | Beginner-heavy Millions of learners. Mostly juniors helping juniors in forums. | Academic peers Other students in the course. Limited professional network value. | Slack community Active community. But dispersed across many cohorts and programs. | 500+ practicing engineers Amazon, Google, TCS, Accenture. Code reviews, architecture debates, career advice. |
| Pricing Model | ₹8,000–50,000 Per course. Need 5-10 courses for full coverage. Total: ₹40,000–5,00,000 | $49–79/month ₹4,000–6,500/month. Annual: ₹48,000–78,000 for access to courses. | $399–1,600/month ₹33,000–1,30,000/month. Total program: ₹1,30,000–5,00,000+ | ₹9,999/year Flat annual rate. 50+ live sessions. All tracks. No hidden costs. |
| Outcome Focus | Certificate of completion PDF certificate. Employers don't care. No proof of real skills. | University certificate Verified cert from Stanford/MIT. Signals learning, not building. | Nanodegree credential Industry partnerships. But still a credential, not deployed systems. | Production portfolio 25+ deployed systems. GitHub repos. Live URLs. Proof you can build, not just learn. |
Traditional platforms teach you what AI is. TMLC teaches you how to ship AI to production. Every session ends with a deployed system. You build 2 production systems in your first month — while others are still watching intro videos. By attending sessions regularly over a year, you'll have 25+ production systems in your portfolio — not 6 course certificates.
Live sessions and project builds are conducted around five core themes covering everything from data pipelines to production deployment and research.
End-to-end data pipelines for AI systems — scalable ingestion, validation, transformation, and feature pipelines built for real-world constraints. Designed to power reliable, outcome-driven AI models in production.
Learn foundation, fine-tuned, and domain-adapted models engineered for real-world performance. From LLMs and multimodal systems to classical ML and deep learning, optimized for accuracy, cost, latency, and measurable business outcomes in production.
Combining advanced tooling, intelligent agents, and robust orchestration. From LLM integration and vector databases to planning, memory, routing, state management, and multi-agent coordination — built for secure, scalable, and outcome-driven AI in production environments.
Learn production-grade infrastructure, CI/CD for AI, monitoring, logging, evaluation, and cost governance. Built to ensure reliability, scalability, traceability, and continuous improvement of AI systems in real-world environments.
Turning state-of-the-art research into deployable AI systems. From paper analysis and rapid experimentation to engineering, hardening, and shipping reliable models that deliver measurable impact in production.