Mastering MLOps
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.
Online
Course
3 Months
Live Lectures
Advanced
Sep third week, evenings
₹ 50,000 + 18% GST
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. .
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.
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
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
MLOps
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.
Capstone
Project
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
