to Build, Develop
and Deploy AI/ML Applications at Scale
Offered by TalentSprint in collaboration with CCE at IISc
MLOps, a discipline that unifies AI and ML Systems development and deployment to streamline the continuous delivery of high-performing models in production.
The Advanced Certification Course in AI and MLOps is offered by TalentSprint in collaboration with CCE at IISc. CCE at IISc 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
TalentSprint offers AI and MLOPs course in collaboration with CCE at IISc 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 members and provides an MLOps certification from CCE at IISc upon completion.
In addition to the online live interactive format, the course also offers an opportunity for 4 days of campus visits at IISc. 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 members 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.
Yes, if you are
Application Fee* ₹2,000
Programme Fee* ₹4,00,000
Programme Fee with Scholarship ₹3,00,000
Check my
eligibility for the scholarship
(*18% GST extra as applicable)
*Fees paid are non-refundable and non-transferable.
Special
Program Fee for Corporate Nominations**
**Applicable only for enterprises nominating their employees as a group
Modes of payment available
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 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 Advanced Certification Programme in AI & MLOps offered by TalentSprint in collaboration with CCE at IISc enables professionals with an in-depth understanding of MLOps, its tools, and best practices for implementation.
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 members 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.