Frequently Asked Questions

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.

• IISc edge: Get certified by IISc, India's #1 University and #1 Research Institute*
• High Impact Format: Leverage distinctive pedagogy of LIVE Online Sessions, mini projects, and capstone projects.
• Accomplished Faculty: Curriculum taught by accomplished faculty with research credentials from premier institutions like MIT-USA, Imperial College-London, and others.
• Hands-On Labs: Apply concepts in practice labs with real data.
• Mentor Support: Opportunity for direct interaction with the faculty and industry mentors.

*As per NIRF Ranking 2024

The programme is ideal for

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

The programme enables you to accelerate your professional growth. It allows you to

• 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

Upon becoming adept at MLOps implementation, you will be able to

• Improve team collaboration
• Streamline operations, enhance RoI
• Increase reliability, performance, and scalability of AI/ML systems

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.

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

The programme will be delivered in an interactive online format, retaining effectiveness while maintaining safety. The format uniquely combines the benefits of an in-class programme with the flexibility and safety of online learning.

• Synchronous programme delivery by expert faculty
• Interact and get doubts answered by faculty
• Peer learning through working in groups with other participants
• Support from mentors for projects
• Re-attend classes through recorded archives
• Access videos easily with searchable and indexed video archives
• Learn from the comfort and safety of your home
• Office hours with one-on-one mentor support

Your peer group will include AI/ML/Data Science practitioners and tech professionals working with leading organisations.

This is an expertise-building programme for experienced professionals. You will obtain a certification from Centre for Continuing Education (CCE) - IISc, India's #1 University and #1 Research Institute*

*As per NIRF Ranking 2024

A certificate of successful completion is provided upon completion of all programme requirements. All examinations and evaluations related to the certification are carried out by the Centre for Continuing Education (CCE), IISc.

The programme format with LIVE Interactive Sessions, Mini Projects, Industry Capstone Projects, Hands-On Labs, One-on-One interaction with Faculty and Industry Experts and Campus immersion enables a conducive environment to network with your peers in the cohort. Most participants can continue to network even after the cohort by using the alumni portal.

As an institution of national importance, IISc was recognised as an Institution of Eminence (IoE) by the Government of India in 2018 and has been consistently ranked among the top Indian institutions in world university rankings. Hence this is a fully approved certification programme offered through IISc.

All participants must execute, before the start of the program, an agreement that consists of the standard programme Terms and Conditions. It has the following components:

Part A:

• Etiquette and Platform Rights
• Classroom Etiquette & General Policy Guidelines
• Tools and Platforms in Use
• Tools and Platforms: Terms of Use

Part B:

• Honor Code

Part C:

• Certification

Part D:

• Programme Fee, Refund, and Termination Policy

Candidates will be selected based on the details of education, work experience, and a statement of purpose submitted along with the application.

A Relationship Manager will be assigned to the applicant, who will gather all the necessary information and represent his/her candidature to the selection committee for review. The profiles will be sent to IISc for selection.

Applicants must

• Hold a B.E/B.Tech/ M.E/M.Tech or an equivalent degree with a minimum 50% marks.
• Have a minimum of 2 years of professional experience.
• Have coding knowledge

Join in these easy steps:

• Share your details and apply for the programme
• Submit all the necessary documents
• The programme selection committee will evaluate your candidature
• Upon selection, complete your enrollment and join the programme

Module 0: Maths and Programming Pre-requisites

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

Module 1: Foundations of Machine Learning and Artificial Intelligence

• The AI/ML Process and workflow - How to solve a problem using data and algorithms?
• What are the problems solvable by ML/AI? What cannot be solved?
• Data Types and State of the Art Models
  1. Tabular Data - Gradient Boosted Models
  2. Image Data - Convolutional Neural Networks
  3. Sequential and Time Series Data - Recurrent Neural Networks
  4. Text Data - Transformers
  5. Generative AI – GPT class of models for text, Diffusion for images/video
  6. Robotics and other niche areas - Reinforcement Learning
• Decision Tree and Gradient Boosted Models - State of the Art for Tabular Datasets
• The first neural network - A very shallow sigmoidal NN (or Logistic Regression)
• The Mathematics of ML and AI - Empirical Risk Minimization, Gradient Descent and Back Propagation
• A Deep Neural Network: Neurons, Layers, Activation Function, Loss Function, Weights and Biases, Minibatch, Training Algorithms (Momentum, AdaGrad, ADAM), Weight Initialization
• Keras: Finding data, building a model, training a model, model evaluation
• Deep Dive into model selection, evaluation and fine-tuning

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

Applications are closed.

It's a 9-month executive programme.

The programme includes

• Live Interactive Sessions by IISc Faculty
• Saturday 9.00 AM to 1.00 PM 
• Sunday 9.30 AM to 1.00 PM 
• Two campus visits of 2 days each, Bangalore
• Guest Lectures and Mentor Support by Industry Professionals 
• Best-in-class Self-learning Content, Mini Projects, Hands-On Labs and Industry Capstone Projects

The programme will be delivered through faculty-led interactive live sessions on TalentSprint’s ipearl.ai. These online sessions will be held at a schedule convenient for working professionals. Participants will be able to interact with the faculty through video, audio, and chat. They will also be able to chat with fellow participants of the programme during online sessions.

As per the programme norms, participants have to maintain at least 75% of attendance during the programme. Going by the curriculum, it is recommended that participants maintain attendance for enriched learning. In the event of unavoidable circumstances, if any, one can access the recorded lectures on our platform to cover up for the missed sessions.

The programme includes continuous formative and summative assessments. The final scores will be determined based on the sum of scores of various assessments.

Yes, participants are required to have their laptops.

Reading material (pre/post session) will be shared regularly through the Online Learning Management System. The access will be available for 12 Months from the date of the commencement of the programme.

A certificate of successful completion is provided upon completion of all requirements of the programme. IISc carries out all examinations and evaluations related to the certification.

For programme fee-related details, please visit fee page You can also contact programme RM Ramesh to know more. Campus visit fee will be based on actuals and to be borne by the participants.

Flexible EMI options are available that enable you to pay the programme fee in instalments. Please reach out to your RM to know more about the schemes available.

Yes. For scholarship-related details, please visit here. You can also reach out to your RM for more details.

No, the application fee is non-refundable.

No. The programme fee is non-refundable and non-transferable under any circumstances.

Professionals have multiple options for funding.

• Sponsored by employer
• Self-funded: Based on our experience of enabling 3000+ professionals who have participated in our executive programs, self-funding is the most preferred option with over 80% choosing this.

Three key reasons why professionals opt for it:

• Freedom: To opt for a programme of their choice, at a time of their choice, with an institution of their choice and not be restrained by the organisation’s policy.
• Flexibility: To pursue greater career opportunities well even beyond their current employer or to nurture their entrepreneurial ambitions.
• Funding: Access to flexible EMI schemes (paying as low as Rs. 5,550 per month).

We have also observed that the commitment levels were higher among self-funding participants.

iPearl.ai (Interactive Platform for Experiential and Remote Learning) is created by TalentSprint’s engineering team led by its Chief Technology Officer, an IIT Kanpur alumni. The platform integrates best-in-class open source technologies and SaaS services and perfected them over the last seven years.

When this platform was built, the objective was to offer it to TalentSprint’s partners and customers for digital learning and delivery. Since 2013, the platform has been extensively used by IIM Calcutta, IIT Kanpur, IIIT Hyderabad and IIT Hyderabad, and many more of our customers and partners. To date, more than 300,000 students have used this platform, which is reputed for user experience, quality, and scale.

This feature-rich platform has become so popular that some of the partners are now using it for their academic needs.

The tried and tested direct-to-device platform enables 24 x 7 learning for its users. Some key benefits of the platform are:

• Intuitive student experience through a user-friendly LMS
• Seamless faculty experience through state-of-the-art Studios
• Highly Interactive Online Classes
• Effective group/peer learning/projects through Breakout Rooms
• Proctored Online Assessments with detailed analysis
• Insightful Reports on student engagement and performance
• Flexible integration with other components, as needed

A comparative study of iPearl.ai against Canvas, Impartus, Moodle, Google Classroom, Microsoft Teams, and Zoom was conducted. Features in the following categories were compared:

• Instructor-led Learning
• Self-paced Learning
• Assessments/Evaluations
• User Experience
• Customer Support
• Scalability and Performance
• Integration and Customization
• Others

Do not worry. In case you miss a session, you will be given access to view the recorded version of the session soon.

• An internet-connected device, Computer/Laptop/Tablet/Smartphone, is enough to access the platform.
• Web camera and Mic with necessary power backup will be needed for proctored online assessments.

IISc (Indian Institute of Science) is the oldest and the finest higher education institute of its kind in India. It pursues excellence in research and education in several fields of Science and Engineering and is one of the first three publicly funded institutes to be awarded the Institute of Eminence status. The alumni of IISc hold significant academic and industry positions around the globe. For more information, visit https://www.iisc.ac.in.

The programme will be delivered by IISc’s Centre for Continuing Education (CCE). CCE delivers courses suitably designed to meet the requirements of target groups: Research & Development (R&D) Laboratories and Industries, Research Scientists/Engineers, to enable them to grow into competent managers of technology intensive and data-driven organisations. For more information visit http://cce.iisc.ac.in.