to Build, Develop
and Deploy AI/ML Applications at Scale
MLOps, a discipline that unifies AI and ML Systems development and deployment to streamline the continuous delivery of high-performing models in production.
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
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
Learn from accomplished IISc Faculty with research credentials from world
renowned institutions
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.
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.
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 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 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 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 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 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 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 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 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 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 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.
Yes, if you are
*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
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
12 Month 0% Interest Scheme / Interest-Based Schemes
EMI as low as ₹9,530/Month
Loan Partners
Many organisations are dipping their toes in new-age technologies by heavily investing in AI and Machine Learning. However, that does not mean they are reaping the value of deeptech within their organisations.
Between 2022 and 2029, the global machine learning (ML) industry is projected to grow at a compound annual growth rate (CAGR) of 38.8%. Additionally, over 82% of companies leverage machine learning models to enhance productivity within their organizations.
As per studies, 90 % of all AI/ML models never make it into production. The reason is the lack of the right leadership to lead MLOps implementation, which is instrumental for the success of these projects.
MLOps or Machine Learning Operations (MLOps) allows organisations to alleviate many of the issues on the path to AI with ROI and ensures your business derives the most value from your AI/ML investments.
The PG Level Advanced Certification Programme in AI & MLOps aims to help professionals build capabilities to lead new-age projects that are heavily dependent on AI/ML.
The PG Level Advanced Certification Programme in AI & MLOps by IISc in association with TalentSprint enables professionals with an in-depth understanding of MLOps, its tools, and best practices for implementation.
*As per NIRF Ranking 2023
The programme enables you to accelerate your professional growth. It allows you to
Upon becoming adept at MLOps implementation, you will be able to
You are eligible if you hold
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.