FDP
Skill Development Programme on Machine Learning Operations (MLOps)
The Department of Artificial Intelligence and Machine Learning, in collaboration with the Skill Development Centre, successfully organised a three-day Skill Development Programme on Machine Learning Operations (MLOps) from 8 to 10 July 2026 at IBM Lab 1, NHCE. The programme was conducted for NHCE faculty members with the objective of equipping them with industry-relevant skills in deploying, monitoring, and managing machine learning models.
The sessions were led by Mr Raghu Prasad, CEO of Kaushalya Technologies, who shared valuable insights into contemporary MLOps practices and workflows. Participants were introduced to the fundamentals of MLOps, machine learning lifecycle management, and the challenges associated with deploying AI solutions in production environments.
The programme covered key topics including version control, data management, experiment tracking, model packaging, and containerisation using industry-standard tools. Faculty members also gained practical exposure to Continuous Integration and Continuous Deployment (CI/CD) pipelines for automated model deployment.
Hands-on sessions on model monitoring, performance evaluation, drift detection, and retraining strategies further enhanced participants’ understanding of production-scale machine learning systems. An end-to-end MLOps project demonstration enabled faculty members to experience the complete workflow, from model development and deployment to ongoing maintenance.
The interactive sessions effectively combined theoretical concepts with practical implementation, making the learning experience both engaging and relevant. Participants appreciated the well-structured approach and hands-on exercises, which strengthened their technical competence and confidence in applying MLOps methodologies.
A total of 46 faculty members attended on the first day, while 37 and 36 faculty members participated on the second and third days respectively, reflecting strong interest and sustained engagement throughout the programme.
The programme successfully enhanced faculty expertise in MLOps and empowered participants to integrate scalable, reliable, and industry-oriented AI practices into teaching, research, and academic projects.