AI, GenAI & MLOps
Move teams from AI curiosity to practical, governed use cases built on good data foundations.
The case for structured ai, genai & mlops training
Faster delivery
Teams that share common practices ship pipelines and reports faster, with fewer handoffs.
Fewer incidents
Engineering discipline reduces broken jobs and bad data reaching stakeholders.
Retention & growth
Clear learning paths help you grow talent from within.
Programmes in this area
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 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.
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.
Machine Learning & MLOpsMLOps Foundations
Taking models from notebook to production: versioning, pipelines, deployment and monitoring.
Machine Learning & MLOpsMachine Learning Fundamentals
Core machine learning concepts, algorithms and evaluation, explained with practical examples.
Machine Learning & MLOpsMachine Learning with Python
Hands-on ML with pandas and scikit-learn, from data preparation to tuned, validated models.
Upcoming batches
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| Generative AI for Data Teams | Wed, 18 Nov 2026 | Bengaluru classroom | Weekday | 3 days | Enquire |
| Generative AI Fundamentals | Sat, 21 Nov 2026 | Online live | Weekend (Sat) | 4 Saturdays | Enquire |
| Multi-Agent Systems: Design & Orchestration | Sat, 21 Nov 2026 | Online live | Weekend (Sat) | 5 Saturdays | Enquire |
| GPU Infrastructure & DGX Fundamentals | Sat, 21 Nov 2026 | Online live | Weekend (Sat) | 4 Saturdays | Enquire |
| Accelerated Data Science with NVIDIA RAPIDS | Mon, 23 Nov 2026 | Online live | Weekday | 3 days | Enquire |
| LLM Fine-Tuning: LoRA, QLoRA & Evaluation | Wed, 25 Nov 2026 | Online live | Weekday | 4 days | Enquire |
Organisations we have trained
Plan your programme
Frequently asked questions
Can the curriculum be customised to our tech stack?
Yes. Every corporate engagement starts with a needs assessment, and modules, labs and capstones are mapped to the tools and cloud your teams actually use.
What delivery formats do you offer?
Live virtual instructor-led training, on-site training at your office in Bengaluru or other Indian cities, and blended formats.
Who delivers the sessions?
Sessions are led by founder Karthick Selvam, a data engineering corporate trainer, and by vetted practitioner trainers from our freelance trainer network.
How do you measure outcomes?
We agree on success measures upfront, typically pre/post assessments, lab completion and capstone evaluation, and share a report with your L&D team.