Mastering MLOps

Optimizing ML Systems Deployment at Scale

This course offers a comprehensive learning experience in industrializing Machine Learning also know as MLOps and ML system deployment optimization, featuring hands-on exercises, practical projects, and interactive discussions to equip participants with practical skills and expertise in effectively managing and deploying machine learning solutions at scale.




3 Months

Mode of Delivery

Live Lectures



Start Date and Time

Sep third week, evenings


₹ 50,000 + 18% GST  

* Terms and Conditions apply

Target Group

Working Professionals from various industries and job roles seeking to enhance their skills and knowledge in MLOps and AI deployment to advance their careers.

Target Group

Employees of Participating Organizations who can benefit from the program as part of their professional development initiatives.

Target Group

Recent Graduates who want to acquire additional skills to improve their employability in the field of AI and MLOps.

Target Group

Career Changers looking to transition into a new career field and need to acquire relevant skills in MLOps and AI deployment.

Target Group

Entrepreneurs and Small Business Owners who wish to enhance their understanding of AI deployment for better management and decision-making in their businesses.

Target Group

Unemployed or Underemployed Individuals seeking to upskill in MLOps to improve their job prospects and career opportunities.

Target Group

People Seeking Career Advancement who are already employed but want to upskill in MLOps to progress in their current career paths.

Target Group

Diverse Learners from various backgrounds, regardless of age, gender, ethnicity, or other factors, who are interested in MLOps and AI deployment.

 Eligibility Criteria

A basic understanding of machine learning concepts and programming is recommended.

Participants should have access to a computer with internet connectivity to engage in the online lectures, exercises, and practical sessions.

Individuals must have a genuine interest in learning MLOps and optimizing ML systems deployment at scale.

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. .

Write your awesome label here.

Mini Projects

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


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
ML System Design

Understanding the importance of MLOps in AI projects and exploring the AI deployment lifecycle, with a focus on challenges and best practices.

ML System
Architecture & Design

Building efficient ML system architectures with parallel processing and distributed computing, along with optimization techniques and case studies.

Data Engineering for 

Exploring data engineering fundamentals, data pipelines, versioning, management, quality, and validation strategies in ML systems.

CI/CD for
ML Systems

Implementing Continuous Integration (CI) for ML projects, automated pipelines for training and testing, version control for models, and Continuous Deployment (CD).

 Model Monitoring &
Performance Tracking

Understanding model monitoring, performance, and data drift, while addressing model failures in production and continuous improvement techniques.

Scalable Model
Serving & Inference

High-performance model serving strategies, Kubernetes for AI deployment, scaling, load balancing, and cloud-based model deployment.

Security and
Governance in MLOps

Ensuring secure ML systems, addressing data privacy, access controls, authentication, and ethical AI deployment.

Managing & Scaling
MLOps Infrastructure 

Infrastructure management for large-scale ML deployments, auto-scaling, resource allocation, cost optimization, and real-world case studies.


This project allows you to apply your learning in a real-world scenario, serving as the culmination of the course. It provides an opportunity to demonstrate understanding and practical skills in MLOps.

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.