Generative AI, Agentic AI and NVIDIA GPU programmes for teams.
From prompts and RAG to production agents and GPU-optimised inference: hands-on programmes built for Indian enterprise teams.
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RAG Engineering: Retrieval-Augmented Generation in Production
Design, build and evaluate RAG systems that answer reliably from your own documents, with citations and access control.
Agentic AIBuilding AI Agents with LangGraph & CrewAI
Build tool-using agents and agent teams with two popular open-source frameworks, and learn when to use each.
Multi-Agent Systems: Design & Orchestration
Design systems where several specialised agents collaborate: orchestration patterns, communication, evaluation and governance.
Building and Serving LLM Apps with NVIDIA NIM & NeMo
Deploy models as NVIDIA NIM inference microservices and customise, evaluate and guard them with NeMo tools.
GPU Programming with CUDA
Learn to write and optimise GPU code with CUDA, NVIDIA's parallel computing platform, in C++ and Python.
Generative & Agentic AI for Leaders
A non-technical programme for executives and managers: what GenAI and agents can do, how to pick use cases, and how to govern risk.
Generative AI
Generative AI for Data Teams
LLM fundamentals, prompt design, retrieval-augmented generation and responsible use of GenAI on enterprise data.
AI Assistants at Work
Practical, safe use of LLM-based AI assistants for drafting, summarising, analysis and everyday productivity.
AI Assistants for Power Users (Advanced)
Go beyond basics: structured prompting, document workflows, custom instructions, tool integrations and team guidelines.
Generative AI Fundamentals
How large language models work, what they can and cannot do, and how to apply them responsibly in business.
LLMOps: Deploying LLM Applications
Take LLM applications to production: serving, evaluation, observability, cost control and security.
RAG Engineering: Retrieval-Augmented Generation in Production
Design, build and evaluate RAG systems that answer reliably from your own documents, with citations and access control.
Generative AILLM Fine-Tuning: LoRA, QLoRA & Evaluation
Adapt open-weight language models to your tasks with supervised fine-tuning and parameter-efficient methods, then prove the improvement.
Prompt Engineering for Teams
Practical prompt design, structured outputs and verification habits for professionals and developers using LLMs daily.
Generative & Agentic AI for Leaders
A non-technical programme for executives and managers: what GenAI and agents can do, how to pick use cases, and how to govern risk.
Agentic AI
Agentic AIAgentic AI with LangChain & LangGraph
Build tool-using agents and stateful workflows with LangChain and LangGraph, with evaluation and guardrails.
Agentic AIBuilding AI Agents with LangGraph & CrewAI
Build tool-using agents and agent teams with two popular open-source frameworks, and learn when to use each.
Multi-Agent Systems: Design & Orchestration
Design systems where several specialised agents collaborate: orchestration patterns, communication, evaluation and governance.
Agentic AI for Business Process Automation
Identify, design and pilot agent-driven automations for operations, support and back-office processes, safely.
NVIDIA & GPU AI
GPU Programming with CUDA
Learn to write and optimise GPU code with CUDA, NVIDIA's parallel computing platform, in C++ and Python.
Accelerated Data Science with NVIDIA RAPIDS
Speed up pandas, scikit-learn-style ML and Spark workloads on GPUs with the open-source RAPIDS libraries.
Building and Serving LLM Apps with NVIDIA NIM & NeMo
Deploy models as NVIDIA NIM inference microservices and customise, evaluate and guard them with NeMo tools.
Agentic AI on the NVIDIA Stack
Build and profile AI agents using NVIDIA NIM models and the open-source NeMo Agent toolkit alongside frameworks such as LangGraph.
Inference Optimisation with TensorRT & TensorRT-LLM
Make models faster and cheaper to serve: TensorRT for deep learning inference and TensorRT-LLM for large language models.
Model Serving with NVIDIA Triton Inference Server
Serve models from multiple frameworks at scale with Triton: model repositories, dynamic batching, ensembles and monitoring.
GPU Infrastructure & DGX Fundamentals
Understand and operate AI infrastructure: GPUs, DGX systems, networking, scheduling and monitoring for AI workloads.
Pick a role. Follow the path.
GenAI Engineer
Prompt engineering → RAG engineering → LLM fine-tuning → LLMOps
Agentic AI Developer
GenAI fundamentals → AI agents with LangGraph & CrewAI → Multi-agent systems
GPU / Inference Engineer
CUDA → TensorRT & TensorRT-LLM → Triton → NIM & NeMo
AI Leader
GenAI & agentic AI for leaders → AI transformation
AI & GenAI guides
Generative AI vs Traditional AI: What Is the Difference?
Traditional AI mostly predicts or classifies. Generative AI produces new content. Both have a place in the enterprise.
What Is Prompt Engineering?
Prompt engineering is the practice of writing instructions that get reliable, useful output from a language model.
What Is ChatGPT and How Does It Relate to Other AI Assistants?
ChatGPT is a conversational AI assistant from OpenAI, built on large language models. It is one of several similar assistants.
NVIDIA & GPU Computing guides
What Is CUDA?
CUDA is NVIDIA's parallel computing platform and programming model for running general-purpose code on NVIDIA GPUs.
What Is NVIDIA RAPIDS?
RAPIDS is a suite of open-source libraries from NVIDIA for GPU-accelerated data science and analytics.
What Are NVIDIA NIM and NeMo?
NIM and NeMo are parts of NVIDIA's software stack for generative AI: NIM for serving models, NeMo for building and customising them.
TensorRT, TensorRT-LLM and Triton Explained
Three NVIDIA technologies for fast, scalable AI inference.
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