Honours in DevOps and MLOps | Honours in DevOps and MLOps

Honours in DevOps and MLOps

Computer Engineering | B.Tech (Comp)

At the same time, Machine Learning systems are becoming critical components of modern applications. Managing the lifecycle of machine learning models from data collection and training to deployment and monitoring requires specialized operational practices known as MLOps.
The Honours Programme in DevOps and MLOps aims to equip students with practical and theoretical knowledge of modern automation, cloud infrastructures, and pipeline design. Moving systematically from core infrastructure provisioning to scalable cloud native applications, the curriculum ensures that students are prepared for specialized professional roles at the intersection of systems development, software engineering, and intelligent operations.

On this page

The increasing adoption of cloud computing, microservices architecture, artificial intelligence, and large-scale data systems has transformed the way modern software applications are developed and deployed. DevOps practices enable organizations to automate software development, testing, deployment, and monitoring, resulting in faster delivery and improved reliability.

Objectives

The offered programme aims to give the understanding of:

  • To introduce the fundamental principles and operational methodologies of DevOps and MLOps within collaborative corporate settings.
  • To train students on industrial automation tools, continuous integration and delivery (CI/CD) pipelines, and containerization strategies.
  • To provide comprehensive hands-on exposure to deploying, scaling, and managing cloud infrastructure using AWS and Microsoft Azure platforms.
  • To enable students to effectively manage the lifecycle of machine learning models from systematic data pipelines and version tracking to containerized API serving and continuous production drift monitoring.

Learning Outcomes

  • Design and implement automated, reliable continuous integration and continuous deployment (CI/CD) workflows for multi-tier microservices.
  • Provision, orchestrate, and manage cloud infrastructures securely using industry-standard virtualization, container architectures, and infrastructure-as-code methodologies.
  • Build, operationalize, and automate repeatable data validation, model tracking, and training pipelines across robust cloud environments.
  • Deploy machine learning applications successfully as scalable production endpoints and integrate systematic monitoring for performance drift, data variance, and resource health.

List of Courses

  • Foundations of DevOps and Cloud
  • Advanced DevOps and Infrastructure Automation
  • MLOps Foundations
  • Production AI Systems

Eligibility Criteria

Students who have successfully passed the First Year of Engineering in Computer Engineering, AI&DS, CCE, CSBS, EXCP, EXTC, and IT.

Credit Scheme

Course Code Course Name Teaching Scheme (Hrs.)
TH – P – TUT		 Total (Hrs.) Credits Assigned
TH – P – TUT		 Total Credits Suggested Semester of Honours’ Degree
316H05C401 Foundations of DevOps and Cloud 3 – 0 – 0 03 3 – 0 – 0 03 IV
316H05L401 Foundations of DevOps and Cloud Laboratory 0 – 2 – 0 02 0 – 1 – 0 01 IV
316H05C501 Advanced DevOps Engineering and Infrastructure Automation 3 – 0 – 0 03 3 – 0 – 0 03 V
316H05L501 Advanced DevOps Engineering and Infrastructure Automation Laboratory 0 – 2 – 0 02 0 – 1 – 0 01 V
316H05C601 Fundamentals of Machine Learning Operations (MLOps) 3 – 0 – 0 03 3 – 0 – 0 03 VI
316H05L601 Fundamentals of Machine Learning Operations (MLOps) Laboratory 0 – 0 – 2 02 0 – 0 – 2 02 VI
316H05C701 Production AI Systems 3 – 0 – 0 03 3 – 0 – 0 03 VII
316H05L701 Production AI Laboratory + Capstone Project 0 – 0 – 2 02 0 – 0 – 2 02 VII
Total 12 – 4 – 4 20 12 – 2 – 4 18  

*All courses are structurally project/mini-project based; no separate external project needed.

Assessment Method

Evaluation is done by a variety of tools including On-Screen tests, MCQs (multiple-choice questions), Internal Assessment tools and End Semester Examinations etc. Mini-Projects are offered in courses also to encourage project based learning among students.

Key Information

Duration: 3 Years (Parallel with Major B.Tech Degree from Sem IV to VII)

Total Credits: 18

Programme Code: H05

Course Type: Honours’ Degree Program

Mode of Study: Full time (Classroom & Laboratory Integrated)

Campus: Vidyavihar - Mumbai

Institute: KJSSE

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