Next-Gen AI

Advanced Parallel Computing for Machine Learning

This course provides learners with a thorough understanding of parallel computing techniques for machine learning and insights into integrating Federated Learning for decentralized AI systems. 

Format

Online Course

Durati0n

10 Weeks

Mode of Delivery

Live Lectures

Level

Intermediate

Start Date and Time

Sep first week, evenings.

Price

₹ 75,000 + GST  

*Terms and Conditions Apply

Target Group

Data Scientists: Professionals working with large datasets and complex machine learning tasks, seeking to optimize their models through parallel computing.

Target Group

Machine Learning Engineers: Engineers involved in the development, deployment, and scaling of machine learning solutions, aiming to leverage parallel computing for improved efficiency.

Target Group

Software Developers: Developers with a background in AI and machine learning, interested in understanding and implementing parallel computing techniques.

Target Group

Researchers: Academic researchers and industry practitioners exploring advanced AI algorithms and parallel computing methods to enhance their research outcomes.

Target Group

AI Specialists: Individuals specializing in artificial intelligence and looking to expand their expertise in the domain of parallel computing for AI applications.

Target Group

IT Professionals: Professionals in the IT industry with a strong interest in machine learning and parallel computing for AI-driven solutions.

Target Group

AI Enthusiasts: Individuals passionate about artificial intelligence and keen to explore parallel computing as a means to optimize AI models and algorithms.

 Eligibility Criteria

Participants should have basic programming knowledge (Python is used throughout the course) and familiarity with ML concepts."

Basic computer skills, including familiarity with operating systems, file management, and basic software usage, are required to navigate and complete course assignments effectively.

This course is specifically designed for beginners in parallel computing; therefore, participants with little or no prior experience in parallel programming or distributed systems are welcome to join.

What is included ?

Interactive Lessons

Engage with interactive lessons that incorporate multimedia elements like videos, quizzes, and exercises to enhance your understanding and retention of the material.

Discussion Forums

Participate in online discussion forums where you can connect with fellow learners, share insights, ask questions, and engage in meaningful discussions related to the course content. .

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Practical Assignments

Participate in practical assignments and projects that reinforce learning, allowing you to apply acquired concepts and skills. Gain hands-on experience and build confidence in the subject matter.

Assessments 

Regular assessments and quizzes evaluate your understanding of the course material, track your progress, and provide valuable feedback on your learning journey.

Course Overview

Introduction to Parallel Computing &
Machine Learning

Understanding the significance of parallel computing in AI, overviewing ML algorithms, and setting up the development environment.

Parallelism in
Machine Learning Algorithms

Identifying parallelism opportunities in common ML algorithms, implementing parallel versions with OpenMP, and evaluating performance gains.

GPU Acceleration with
CUDA Programming

 Utilizing GPU architecture and CUDA to parallelize ML algorithms, optimizing ML code for GPU acceleration.

Cache Memory
Optimization for ML

Enhancing performance by optimizing cache memory usage in ML algorithms.

Distributed Systems &
 MPI for ML at Scale

Scaling ML algorithms on multiple nodes using MPI for distributed data processing and model synchronization.

Federated Learning
Decentralized AI

Understanding Federated Learning, implementing privacy-preserving ML, and building a Federated Learning system.

Parallel ML Libraries &
Frameworks

Overview of popular parallel ML libraries, accelerating training and inference with parallel libraries.

TensorFlow Distributed Strategies &
Future Directions

Applying TensorFlow distributed computing for large-scale ML, exploring data and model parallelism, and discussing future trends in Next-Gen AI, Parallel Computing, and Federated Learning.

Tech Stack

Throughout the course, you will gain practical experience and proficiency in using these tools to effectively manage, deploy, and optimize machine learning solutions at scale.

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Prof. Sashikumaar Ganesan

Chair, Dept. Computational and Data Sciences
IISc Bangalore

Program Director

Prof. Sashi, a dedicated educator with extensive expertise, takes great pleasure in mentoring graduates and working professionals alike. He has delivered enlightening courses on Artificial Intelligence, Machine Systems and Industrializing ML (MLOps) to over 750 working professionals, earning admiration from students and industry experts.