1. The Growing Demand

MLOps, a discipline that unifies AI and ML Systems development and deployment to streamline the continuous delivery of high-performing models in production.

  • MLOps market expected to reach US$4 billion by 20251
  • By 2025, 100% of enterprises will be using some sort of AI2
  • 97 million new jobs will be created with the appliance of AI by 2025.3
  • MLOps professionals command upto
    ₹1 Cr packages4

2. High Impact Format

  • Interactive Live Sessions With expert faculty from IISc
  • Hands-on Labs Apply concepts with real data
  • Mini Projects Mentor support by industry professionals
  • Capstone Projects Supported by IISc faculty and industry mentors
  • One-on-One Office Hours With IISc faculty and industry mentors
  • Experience IISc campus Two campus visits of 2 days each

3. IISc Edge - AI and MLOps Course
India's #1 University and #1 Research Institute*


The PG Level Advanced Certification Course in AI and MLOps will be delivered by IISc’s Centre for Continuing Education (CCE). CCE delivers courses suitably designed to meet the requirements of various target groups, eg: research & development (R&D) laboratories and industries, research scientists and engineers, to enable them to grow into competent managers of technology-intensive and data-driven organizations. For more information, visit cce.iisc.ac.in

Hear from our programme alumni

AI and MLOPs Course Overview

IISc offers an online live interactive AI and MLOps course for professionals looking to advance their skills in machine learning operations. The course covers advanced machine learning concepts and provides hands-on experience in MLOps. The course is designed by distinguished IISc faculty and provides an MLOps certification from IISc upon completion.

In addition to the online live interactive format, the course also offers an opportunity for campus visits. This unique combination of online and on-campus learning ensures a holistic learning experience.

The AI and MLOps course is ideal for professionals looking to build expertise in end-to-end machine learning systems for real-world applications. With a focus on ML operations, the course provides students with the knowledge and skills to design, build, deploy, and scale AI/ML models at scale using industry-standard MLOps tools and techniques.

IISc Campus Visit

Expert Faculty

Learn from accomplished IISc Faculty with research credentials from world
renowned institutions

  • Prof. Sashikumaar Ganesan

    Ph.D., Computational Mathematics, OvGU Germany

    Sashikumaar Ganesan is a Professor in the Department of Computational and Data Sciences (CDS) at the Indian Institute of Science (IISc), Bangalore, and the founder of Zenteiq Edtech Pvt. Ltd., a pioneering deep tech startup. With extensive experience in Computational Science, Scientific Machine Learning (SciML), and Data Science, he specializes in the development of scalable ML algorithms, distributed training, and cloud computing through Machine Learning Operations (MLOps). He has earned a reputation for his innovative approaches in integrating Finite Element Analysis, Scientific Computing & Machine Learning, and High-Performance Computing. Before joining IISc, he held esteemed positions as a Research Associate at Imperial College London and as an Alexander-von-Humboldt fellow at WIAS Berlin, after earning his Ph.D. from Otto-von-Guericke University, Germany. Professor Ganesan's wealth of knowledge and multifaceted expertise make him a leading figure in AI and MLOps, driving advancements in the field.

  • Programme Coordinator Prof. Deepak Subramani

    Ph.D., Massachusetts Institute of Technology (MIT), USA

    Prof. Deepak, an assistant professor in the Dept of Computational and Data Sciences, IISc and an alumnus of MIT and IIT Madras, is a renowned data scientist and AI expert. He develops and applies machine learning and artificial intelligence techniques to solve complex engineering and environmental problems. His notable contributions include foundational models and GenAI for autonomous robots, weather forecasting, satellite image analytics, and renewable energy transition. He is also a passionate teacher and mentor, committed to training the next generation of scientists, engineers and industry practitioners.

AI and MLOps Course Curriculum

The AI and MLOps course at IISc provides a state-of-the-art curriculum designed by distinguished faculty to teach you how to build, deploy, and scale AI/ML models at scale

Module 0: Maths and Programming Pre-requisites

  • Probability & Stats
  • Calculus & Linear Algebra (Tensors)
  • Python, TensorFlow (Tensor operations)
  • Data Munging (Tabular Data)

Module 2: Computer Vision

  • Essential Tasks in Computer Vision
  • Convolutional Operation - kernels, padding, feature maps
  • Pooling Operation
  • CNN for Image Classification
  • Transfer Learning – Backbone of modern CV
  • Residual Connection, Batch Normalization for training deeper networks
  • Depthwise Separable Convolution and Xception
  • Object Localization and Detection Algorithms - YOLO
  • Image Segmentation - UNet and DeepLab
  • Computer Vision in Operation – Best practices and tools

Module 3: Designing Machine Learning Systems

  • Cloud Computing foundations
  • Need for cloud computing
  • Introduction to AWS and Azure systems
  • Cloud9 and EC instances
  • AWS sage maker
  • Cloud Computing Tools - AWS ML Tools; Google Cloud; IBM Watson

Module 4: Practical MLOps

  • MLOps & Version Control
  • What is DevOps and MLOps
  • What is CI and CD why do we need them ?
  • What is version control - git basics - clone, pull, push, brach, commit, merge conflicts
  • Docker - Need for containerisation of applications
  • How to setup a docker container
  • How to mount volumes and enable ports within docker
  • How to write a docker compose file
  • CI & DVC - Github actions - Data Version Control - DVC Pipelines
  • MLOps Tools and ML Deployment - Jenkins - Dags Hub - Weights and Biases or Mlflow
  • Basics of python flask for web deployment

Module 5: Natural Language Processing

  • Essential Tasks in NLP
  • Data Preprocessing for Language Models
    1. Text Vectorization Layer
    2. Standardization, Vocabulary Indexing
    3. Embedding Word Vectors
    4. TF-IDF
  • Tokenization fundamentals, Byte Pair Encoding
  • Bag of Words Model and Sequential Models
  • Attention Mechanism
  • Transformer Encoder for Language Comprehension/Understanding tasks
  • BERT Models

Module 6: Representation Learning, Generative Models and Research Trends

  • Representation Learning: The core of modern AI
  • Decoder only GPT class of models for Language Generation tasks
  • LLMOps – Tools, Platforms (incl, but not limited to LangChain, OpenAI API)
  • Prompt Engineering, RAG, LoRA (and variants)
  • Generative Adversarial Networks, Diffusion Models for image generation
  • Autoencoders for representation learning, pre-training
  • Reinforcement Learning through Human Feedback
  • Guardrails and processing of LLM outputs

Module 7: Parallel Computer Architecture and Programming Models

  • Parallel Programming Paradigms: OpenMP
  • Scoping Variables and race conditions
  • Parallel Algorithms using openMP: kmeans; histogram computation; Basic problems on loop interchange and blocking
  • Parallel Programming Paradigms: MPI
  • Cuda
  • Submitting jobs on Cluster
  • Python Parallel Programming - Multi Threaded vs Multi Processor in python - GIL -
  • Introduction to PyOMP - Introduction to PythonMPI

Module 8: Machine Learning at Scale

  • GPU's in ML - Introduction to GPU architecture - Tensor cores and Mixed precision -
  • Distributed computing with tensorflow (GPU)
  • Parallel ML Libraries: Dask, Horovod, Ray, RAPIDS
  • Introduction to cuDF, cuML, cuPY

Hands-on Practices & Tools

Capstone Projects

Project 1: MLOps Workflow for Hospital Emergency Prediction using ETS Data on AWS

Project Overview:
The objective of this project is to develop and implement a Machine Learning Operations (MLOps) framework for predicting emergent medical situations at the point of patient admission to a hospital. This predictive model will leverage historical patient data, particularly the Emergency Triage Score (ETS) dataset, to probabilistically anticipate instances where a significant influx of patients is likely to occur. This project will harness data-driven insights to enhance the preparatory capacities of healthcare facilities in responding to emergent medical scenarios, thereby improving overall healthcare service efficiency and patient care outcomes.The goal of this project is to develop a Machine Learning Operations (MLOps) workflow to predict hospital emergencies at the time of patient admission. This predictive model will help healthcare providers allocate resources efficiently and prepare for potential surges in patient volume.

Project 2: Financial Fraud Detection using Deep Learning and MLOps

Project Overview:
This project aims to develop a robust solution for detecting financial fraud using MLOps methodologies. It involves analyzing historical transaction data, customer behavior, and relevant factors to identify and prevent fraudulent activities in the financial domain. The financial sector faces a growing threat from financial fraud, driven by increasingly sophisticated tactics used by fraudsters. Traditional fraud detection methods are struggling to keep up with these evolving schemes. This project addresses the need for advanced fraud detection solutions capable of adapting to emerging threats in real-time.

Project 3: Customer Conversational Intelligence Platform

Project Overview:
A Customer Conversational Intelligence Platform is a system that employs advanced technologies, including machine learning and natural language processing, to analyze and make sense of customer interactions across various communication channels such as chatbots, call centers, emails, and social media, that modern businesses accumulate. This project seeks to utilize this data goldmine to provide businesses with a competitive edge in customer service. Here, we will develop a platform that harnesses the power of machine learning to analyze vast amounts of customer interaction data. The aim is to derive actionable insights from these interactions, optimize customer service processes, and enhance overall customer experience.

Project 4: Driver Demand Prediction for Optimal Food Delivery Charges

Project Overview:
This project aims to predict the demand for delivery drivers in specific regions and times, leveraging MLOps methodologies. By analyzing order requests, driver activity, and related parameters, the goal is to optimize delivery charges, ensuring consistency and minimizing customer drop-offs. The unpredictable nature of delivery charges, primarily due to driver unavailability, often results in increased prices and subsequent customer dissatisfaction. This project seeks to bridge this gap by forecasting driver demand, thereby streamlining delivery pricing.

Project 5: Personalized Financial Advisor using Large Language Model (LLM)

Project Overview:
The field of finance can be complex and overwhelming for individuals seeking personalized financial advice. In order to make informed decisions regarding investments, retirement planning, budgeting, and financial products, individuals often require guidance from financial experts. The aim of this project is to develop an Intelligent Financial Advisor powered by a Large Language Model (LLM) to provide personalized financial advice and guidance to individuals. By leveraging NLP and machine learning techniques, the Intelligent Financial Advisor will assist users in making informed financial decisions and achieving their financial goals.

Project 6: Automated SEO using ChatGPT

Project Overview:
The goal of this project is to create an automated Search Engine Optimization (SEO) tool using ChatGPT, an AI-based chatbot system. The tool will use natural language processing (NLP) and machine learning (ML) algorithms to analyze website content, identify SEO issues, and provide recommendations for improvement. The tool will help website owners and SEO professionals to optimize their website's content and improve search engine rankings more efficiently and effectively.

Project 7: Automated Answer Validation for Science Question Answering using Siamese Text Similarity Model

Project Overview:
In the domain of scientific question answering, validating answers and providing accurate feedback is critical for effective learning. The goal of this capstone project is to develop an automated answer validation system using a Siamese text similarity model. The system will compare student responses with the correct answer and distractors to determine the level of correctness and provide appropriate feedback. The automated answer validation system for science question answering will benefit educators and students in science-related subjects. It will streamline the assessment process, reduce manual effort, and ensure consistent evaluations, leading to improved learning outcomes in the science domain.

Project 8: GAN based interior designs

Project Overview:
A GAN is a popular model for unsupervised machine learning where two neural networks — a generator and a discriminator, interact with each other. The generator generates images out of random noise it takes as input; The discriminator detects whether these generated images are fake or real (by comparing them to the images in a dataset). This process continues for several epochs until the discriminator loss between fake and real achieves its minimum. As the loss reaches the minimum, the generator becomes sufficiently skilled in generating images similar to those in the original dataset. AI-driven innovation with GANs has many applications in creative industries such as design. Be it architectural design, landscape design or interiors design, the possibilities are endless. Such generated designs have the potential to drive rapid growth and profits in the design industry. The goal of this project is to generate realistic new interior room designs by training a GAN network on the IKEA Interior Design Dataset.

Project 9: Image-based plant disease identification

Project Overview:
Crop losses due to diseases are a major threat to food security every year, across countries. Conventionally, plant diseases were detected through a visual examination of the affected plants by plant pathology experts. This was often possible only after major damage had already occurred, so treatments were of limited or no use. Recently, access to smartphone based image capturing has highly increased amongst farmers and agriculturists. This has led to the successful adoption of plant disease diagnostic applications based on deep learning techniques. This is of immense value in the field of agriculture and an excellent tool for faster identification and treatment of crop diseases. It holds key importance in preventing crop based food and economic losses. The goal of this project is to build a convolutional neural network or to use transfer learning and develop a plant disease identification tool.

Project 10: Food Image Segmentation

Project Overview:
Worldwide, obesity has nearly tripled since 1975. In 2016, more than 1.9 billion adults, 18 years and older, were overweight (WHO sources). In such a situation, documenting dietary caloric intake is crucial to manage weight loss. Food image segmentation is a critical and indispensable task for developing health-related applications such as automated estimation of food calories and nutrients as a means for dietary monitoring. One of the challenges in this area is the improvement of accuracy in dietary assessment by food image analysis. However, how to derive the food information (e.g., food type and portion size) from food images effectively is a challenging task and an open research problem. In this project, participants are expected to make a model that can segment the food components present in an input food image and build an application that can predict the food class and the food portions from it.

Project 11: Personalized Financial Advisor using Large Language Model (LLM)

Project Overview:
The field of finance can be complex and overwhelming for individuals seeking personalized financial advice. In order to make informed decisions regarding investments, retirement planning, budgeting, and financial products, individuals often require guidance from financial experts. The aim of this project is to develop an Intelligent Financial Advisor powered by a Large Language Model (LLM) to provide personalized financial advice and guidance to individuals. By leveraging NLP and machine learning techniques, the Intelligent Financial Advisor will assist users in making informed financial decisions and achieving their financial goals.

Is this AI and MLOPs course ideal for me?

Yes, if you are

  • An AI and Data Science practitioner seeking to build expertise in AI and MLOps
  • A Tech professional looking to transition to AI and MLOps
  • A Tech ops professional aspiring to upgrade to AI and MLOps


  • B.E/B.Tech/M.E/M.Tech/M.Sc/MBA or equivalent master's degree with a minimum 50% marks
  • Minimum 2 year of professional experience
  • Basic coding knowledge required

How can I enrol for this programme?

  • Application

    Apply for the

  • Upload Document


  • Selection

    Selection by
    IISc Committee*

  • Enrollment

    Join the

*Selection for the programme will be done by IISc and is strictly based on the education, work experience, and motivation of the participants.

**Scanned copies to be submitted within 7 days 1. Education Certificate 2. Experience Letter/Latest Pay Slip

What is the return on my investment?

Drive business value
for your Organization

  • Streamline operations, improve efficiency and efficacy, enhance RoI
  • Increase reliability, performance, and scalability of AI/ML systems

Accelerate growth
of your Career

  • Design AI/ML models from
    scoping to deployment
  • Identify gaps in creating and
    scaling AI/ML models
  • Evaluate and improve
    AI/ML models for projects
ai ml certification

AI and MLOPs Course Fee

Application Fee ₹2,000

Programme Fee ₹3,80,000 Programme Fee with Scholarship ₹2,85,000 Check my eligibility for the scholarship (18% GST extra as applicable)

Special Pricing for Corporates

Campus visit fee will be based on actuals and to be borne
by the participants

Fees paid are non-refundable and non-transferable.

Modes of payment available

  • Internet
  • Credit/Debit
  • UPI

Easy Financing Options

12 Month 0% Interest Scheme / Interest-Based Schemes

EMI as low as ₹9,530/Month

EMI Options

Loan Partners

Programme Media Coverage

Frequently Asked Questions

You are eligible if you hold

  • B.E/B.Tech/ M.E/M.Tech or an equivalent degree with a minimum 50% marks
  • Minimum 2 year of professional experience and
  • Basic coding knowledge required

You will learn from eminent IISc faculty who are trained in the world's best laboratories and have been a part of some of the global breakthrough discovery and research projects. Meet your faculty here.

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