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
  • 60% enterprises to operationalize workflows with MLOps by 20242
  • 73% of enterprises consider ML adoption as a competitive edge3
  • 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
World’s #1* Research University


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

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.

Expert Faculty

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

  • Prof. Sashikumaar Ganesan

    Ph.D., Computational Mathematics, OvGU Germany

    Chairman of the Department of Computational and Data Sciences at IISc. He co-founded ZenteiQ EdTech, a Higher-Ed focused EdTech startup incubated at SID-IISc. He also worked as a Postdoc Research Associate at Imperial College London, WIAS Berlin, and OvGU Magdeburg (Germany). He authored academic books - Finite Elements: Theory and Algorithms and Finite Element Methods on Moving Meshes for Free Surface and Interface Flows. His research interests include Finite Element Analysis, Parallel Algorithms, Data-Driven Modeling, ML/NN for CFD.

  • Programme Coordinator Prof. Deepak Subramani

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

    Assistant Professor in the Department of Computational and Data Sciences, IISc. An alumnus of MIT and IIT Madras, he is the Founder of ZenteiQ EdTech, a Higher-Ed focused EdTech startup incubated at SID, IISc Bangalore. His research interests include ML/AI for Environmental Forecasting, Data-Driven Routing of Autonomous Vehicles, Bayesian Learning and Data Assimilation, Uncertainty Quantification, and Computational Optimization.

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: Brushing up of mathematics and python

  • Probability & Stats
  • Variables & 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
  • Residual Connection, Batch Normalization for training deeper networks
  • Depthwise Separable Convolution and Xception
  • Object Localization and Detection Algorithms - YOLO
  • Image Segmentation - UNet and DeepLab

Module 3: Natural Language Processing

  • Recurrent Neural Network Basics
  • Solving a time series problem with RNNs
    1. Modeling
    2. Issues and solutions
    3. Common sense baselines and model evaluation
  • LSTM and GRU for long time series
  • Essential Tasks in NLP
  • Data Preprocessing
    1. Text Vectorization Layer
    2. Standardization, Vocabulary Indexing
    3. Embedding Word Vectors
    4. TF-IDF
  • Bag of Words Model and Sequential Models
  • Full range of Bag of Words Models - Naive Bayes to Deep Neural Networks
  • Attention Mechanism
  • Transformer Encoder and Decoder for Neural Machine Translation

Module 4: Representation Learning, Generative Models and Research Trends

  • Representation Learning: The core of modern AI
  • Autoencoders
  • Variational Autoencoders
  • Generative Adversarial Networks
  • Generative Large Language Models
  • Research Trends: Introduction to Reinforcement Learning

Module 5: Parallel Computer Architecture and Programming Models

  • Computer Architectures, Pipelining and super-scalar processor, SIMD vectorization, Caches
  • Multicore architectures, GPUs, Data access optimization,
  • Shared Memory Programming basics, Shared memory programming with OpenMP
  • Message-passing, MPI, CUDA, MapReduce.

Module 6: Machine Learning at Scale

  • Automatic parallelization with Numba, Dask
  • PySpark
  • Distributed training with TensorFlow

Module 7: Cloud Computing Foundations

  • Code Version control, Data version control, ML model version control
  • Devops methodology
  • Cloud Computing Solutions at Scale
  • Cloud Data engineering

Module 8: Cloud Machine Learning Engineering and Operations

  • Introduction to MLOps
  • MLOps for containers
  • Continuous Integration, Continuous Deployment for ML models,
  • CI/CD Integration with Jenkins and Docker.
  • Monitoring, ContinuousTraining and Feedback

Hands-on Practices & Tools

Capstone Projects

Project 1: Fashion Compatibility Prediction

Domain: E-commerce and Fashion

Techniques: Deep Learning (Bi-LSTM)

Overview and Problem Statement:
The fashion domain is a very important and lucrative application of computer vision. According to a recent study by Statista, the fashion industry’s worth was estimated to be $1.5 trillion in 2020 and it keeps growing, representing a huge market for garment companies, designers, and e-commerce entities. Fashion image retrieval and fashion image attribute learning have been the two main areas of study in this domain. The goal of this project is to compose or predict fashion outfits automatically, working to address challenges in compatibility and aesthetics. Using the Polyvore dataset, learn compatibility relationships among fashion items to facilitate effective fashion recommendation with a bidirectional LSTM (Bi-LSTM) model.

Project 2: Food Image Segmentation

Domain: Food / Wellness industry

Techniques: CNN based Image segmentation

Overview and Problem Statement:
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 calorie 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, a model that can segment the food components present in an input food image will be built and an application that can predict the food class and the food portions from it will be designed.

Project 3: Image super-resolution using a Generative Adversarial Network

Domain: R&D (Computer Vision)

Techniques: Generative Adversarial Network

Overview and Problem Statement:
Estimating a high resolution (HR) image from its low-resolution (LR) counterpart is referred to as super-resolution (SR). Super-resolution is a task concerned with upscaling images from low-resolution sizes such as 90 x 90, into high-resolution sizes such as 360 x 360 (upscaling factor of 4x). Recovering the finer texture details of an image while achieving super resolution at large upscaling factors has received great attention in the computer vision research community. Super-resolution holds great importance in recovering photorealistic details lost to resolution effects and can be applied to a number of real life tasks such as CCTV image enhancement for identification of criminals, restoration of old family photos, medical imaging data enhancement, turning a smartphone camera to an SLR, autonomous vehicle vision enhancement etc. The goal of this study is to acquire high resolution images from low resolution images by training a super-resolution generative adversarial network (SRGAN) on the CelebA Dataset, that contains images of celebrities.

Project 4: Identification of Quora question pairs with the same intent

Domain: Online Knowledge Platform

Techniques: NLP, Machine Learning

Overview and Problem Statement:
Quora is a platform to gain and share knowledge on any topic. It allows people to ask questions and connect with people who contribute unique insights and quality answers. This equips people to learn from each other and to understand topics in diverse subjects. Over 100 million users visit Quora every month, and a lot of them inadvertently ask replicate questions, worded differently by different users. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question. This also leads to an inefficient system for writers, as they spend time on answering multiple versions of the same question. The goal of this project is to use NLP, ML algorithms and create an application to classify whether Quora question pairs are duplicate or not. This will be instrumental in providing high quality answers and an improved experience for Quora writers and seekers.

Project 5: Breast cancer classification from digitized FNA image feature measurements

Domain: Healthcare – Cancer diagnostic

Techniques: Classical Machine Learning, Deep Learning

Overview and Problem Statement:
Breast cancer is the most common cancer among women worldwide, accounting for 25 percent of all cancer cases and has affected 2.1 million people in 2015. Breast cancer can be categorized into (i) Benign (noncancerous, non life-threatening) tumors and (ii) Malignant (Cancerous, rapidly invading nearby tissue) tumors. Identification of the benign and malignant conditions is normally achieved by imaging of the targeted area of the body using mammography, computed tomography, MRI, ultrasound etc. which aids in diagnosis by physicians. Identifying benign or malignant tumors through machine learning techniques can provide dramatic improvement in diagnostic accuracy. For this project, the Breast Cancer Wisconsin dataset consisting of fine-needle aspirate (FNA) digitized image feature measurements will be used. The final goal is to use the data to train machine learning classifiers (eg. KNN, SVM, Decision tree, Random Forest, Ensemble Learning) and deep neural networks (CNN), and accurately identify benign vs malignant breast cancer with high accuracy.

Project 6: Video based action classification

Domain: Surveillance & Security

Techniques: CNN, RNN

Overview and Problem Statement:
Applications such as surveillance, video retrieval and human-computer interaction require methods for recognizing human actions in various scenarios. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recordings. Typical scenarios include scenes with cluttered, moving backgrounds, nonstationary camera, scale variations, individual variations in appearance and cloth of people, changes in light and view point and so forth. All of these conditions introduce challenging problems that can be addressed using deep learning (computer vision) models. The given dataset consists of labeled videos of 6 human actions (walking, jogging, running, boxing, hand waving and hand clapping) performed several times by 25 subjects. The goal of the project is to classify the actions performed in the video frames.

Project 7: Anomalous user behavior detection in Information Security Systems

Domain: Information Security Systems

Techniques: ML Engineering

Overview and Problem Statement:
One of the core components of information security systems is the anomalous user behavior detection, that aids in the detection of intrusion, insider threat and authentication system break. Anomalous behavior based alarm to the system administrator can be combined with other information to determine whether it constitutes an unauthorized or malicious use of a resource. The goal of this project is to develop an effective anomalous user behavior detection system by applying the Isolation Forest algorithm to an enterprise dataset.

Project 8: Improving Energy Efficiency using Machine Learning

Domain: Energy

Techniques: ML Engineering

Overview and Problem Statement:
Maintaining optimal energy efficiency is one of the key areas of focus in residential buildings, offices and factories. By considering features like energy prices, equipment maintenance, labor costs and inventory, ML algorithms can schedule the most appropriate time to perform energy-intensive activities. This can enable enterprises to maximize cost savings by running the right processes at the right time. The goal of this project is to develop a machine learning based application to track energy usage patterns over time so as to reduce the amount of wasted energy and thus perform cost savings.

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 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

Details 9 Months Programme
Programme Fee* ₹3,50,000

mlop course12-Month 0% EMI available mlop courseNominate your employees to avail special benefits *GST as applicable

(i) Application Fee of ₹2,000/-, and
(ii) Campus visit fee will be based on actuals and to be borne by the participants

Fees paid are non-refundable and non-transferable.

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.

About TalentSprint

  • 10 Years of
  • 200K Empowered
  • 95% Completion
  • 85 Net Promoter

Established in 2010, TalentSprint is a part of NSE group and a global edtech company that brings transformational high-end and deep-tech learning programs to young and experienced professionals. The company’s digital learning platform ipearl.ai offers a hybrid onsite/online experience to seekers of deep technology expertise. TalentSprint partners with top academic institutions and global corporations to create and deliver world class programs, certifications, and outcomes.Its programs have consistently seen a high engagement rate and customer delight. It is a leading Innovation Partner for the National Skill Development Corporation, an arm of the Ministry of Skill Development and Entrepreneurship, Government of India. A recipient of various prestigious accolades, TalentSprint was recently honored with the Indian Achievers Award 2022, for its excellence in building deeptech talent in India. For more information about TalentSprint, visit TalentSprint website.