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.
Learn from accomplished IISc Faculty with research credentials from world
renowned institutions
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.
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.
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
Details | 9 Months Programme |
---|---|
Programme Fee* With Scholarship |
₹2,70,000 |
12-Month 0% EMI available
Nominate 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.
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.
The machine learning job market is projected to be worth $31 billion by 2024 – at an annual growth rate of 40 per cent over the last six years.
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.