MLOps: From Model Development to Deployment

Short Course

Course Schedule	Duration (days)	Mode of Delivery	TGS Reference Number
To be advised	5	Classroom Learning	TGS-2026064813

Course Overview

The course is to enable participants to architect and operationalise production-ready Artificial Intelligence/Machine Learning (AI/ML) solutions through well-defined Machine Learning Operations (MLOps) workflows. Leveraging advanced knowledge of statistical modelling and core ML architecture, participants will master the skills required to prepare technical blueprints, apply different Machine learning algorithms, and build robust operational and data pipelines to ensure the developed solutions are scalable, reliable, and aligned with defined business and performance metrics throughout the full machine learning lifecycle.

Course Objectives

At the end of the course, participants will be able to:

Data Processing Pipelines: Build and develop robust machine learning data pipelines and manage the computing infrastructure for data processing and model training
Architecture and Development: Define, structure, and architect complete AI/ML solutions and MLOps workflows that ensure the solutions meet all specified metrics throughout the full machine learning lifecycle.
Algorithm Validation: Evaluate and apply machine learning models and algorithms, developing test procedures to evaluate and validate model performance in the intended operating environment.
Solution Deployment: Develop and implement the final solution, ensuring the use of appropriate techniques and cloud-based architectures to meet the contextualized usage scenarios and business requirements.
Responsible MLOps and Security: Define, design, and implement an end-to-end MLOps workflow incorporating robust software engineering principles, continuous model monitoring and feedback loops, fairness, bias, and transparency in pipelines, and security best practices, ensuring the deployed AI/ML solution is reliable, scalable, and resilient throughout the entire operational lifecycle.

Prerequisites

A minimum diploma qualification and basic knowledge of Python are required.

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Funding Available

SkillsFuture Funding

Enhanced Training Support

SkillsFuture Credit

SFEC

PSEA

Course Fees

	Singapore Citizens		Singapore PRs	Enhanced Training Support for SMEs	International Participants
	39 years old or younger	40 years old or older	Singapore PRs	Enhanced Training Support for SMEs	International Participants
Full Course Fee	S$5,250.00	S$5,250.00	S$5,250.00	S$5,250.00	S$5,250.00
Less: SSG Grant Amount	S$3,675.00	S$3,675.00	S$3,675.00	S$3,675.00	-
Nett Course Fee	S$1,575.00	S$1,575.00	S$1,575.00	S$1,575.00	S$5,250.00
9% GST on Nett Course Fee	S$141.75	S$141.75	S$141.75	S$141.75	S$472.50
Total Nett Course Fee Payable, including GST	S$1,716.75	S$1,716.75	S$1,716.75	S$1,716.75	S$5,722.50
Less Additional Funding	-	S$1,050.00	-	S$1,050.00	-
Total Nett Course Fee Payable, including GST, after additional funding from the various funding schemes	S$1,716.75	S$666.75	S$1,716.75	S$666.75	S$5,722.50

Total nett course fee payable, including GST, after additional funding from various funding schemes.
Participants must fulfill at least 70% attendance and pass all assessment components to be eligible for SSG funding.
The applicable funding is for Singaporeans and Singapore PRs. This is subject to change and in accordance with SSG funding guidelines as of course commencement.
Please refer to our Terms and Conditions page for more information.

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