NUS
 
ISS
 
NUS ISS

Data Science Deployment

Move AI models from prototype to production with MLOps, monitoring, governance and responsible deployment

Overview

Part of Graduate Certificate in Predictive Analytics Lifecycle
Duration 3 days
Course Time
Enquiry Please contact ask-iss@nus.edu.sg for more details.

Organisations can build machine learning models, but struggle to turn them into reliable business solutions. Models often remain in notebooks, pilots or isolated prototypes because teams lack the deployment, monitoring, governance and operating practices needed for production AI. Without these capabilities, AI initiatives can be slow to scale, difficult to maintain, and hard for business users to trust.

Data Science Deployment is a 3-day intermediate course that helps you move machine learning and AI models from development into production. As organizations scale their use of machine learning and AI, they face increasing challenges in operationalizing models through CI/CD pipelines, APIs, and cloud-based infrastructure. These challenges require strong MLOps practices to ensure that models can be reliably deployed, updated, and maintained in production environments.

The course focuses on the practical work needed after a model is built: choosing deployment strategies, supporting batch and real-time use cases, managing model serving infrastructure, applying agile delivery practices, and using automation to improve reliability and speed. You will also explore how AI-assisted development tools can help accelerate coding, testing, documentation, troubleshooting and deployment workflows, while still applying human review, governance and responsible AI controls.

By the end of the course, you will be better equipped to help your organisation operationalise AI models, reduce the gap between experimentation and production, improve model reliability, and deliver AI solutions that are scalable, monitored and aligned with business needs.

 

Upcoming Classes

Class 1: 30 November to 2 December 2026 
Duration: 3 Days
When: Nov 30, Dec 1, 2
Mode of Conduct: Face-to-Face
Time: 9:00am to 5:00pm

 

Register

 

Registration Instructions

Self-sponsored Participants

  • Register for the course by clicking on the "Register Now" button above
  • You may refer to the User Guide for Learner

Company-sponsored Participants

  • You will have to be registered for the course by someone from your company who has an account on the LifeLong Learning Portal (L³AP)
  • The person in-charge may register you for the course by:
    • Generating a corporate registration link for you to register for the course
      • After the link is generated, you must:
        1. Log in to L³AP by clicking on the "Register Now" button
        2. Click on the corporate registration link after logging in
      • If you do not follow the above instructions, you will be registered for the course as self-sponsored
    • Registering you for the course backend
      • You will still be required to log in to L³AP and complete your registration by clicking on the "Register Now" button above
  • You may refer your HR/L&D POC to the User Guide for Company

Key Takeaways

At the end of the course, the participants are expected to be able to

  • Design trustworthy AI and machine learning solutions by considering reliability, explainability, governance, bias, business evaluation and responsible deployment needs.
  • Select suitable model deployment strategies for batch, real-time and API-based use cases based on business requirements, technical constraints and operational risk.
  • Deploy machine learning models into production workflows using practical model serving approaches, APIs and deployment patterns.
  • Apply MLOps practices to automate and manage the machine learning lifecycle, including workflow orchestration, versioning, repeatable deployment and maintainability.
  • Monitor production models to track performance, detect degradation, support safe updates and maintain trust after deployment.
  • Use AI-assisted development tools responsibly to accelerate coding, testing, documentation, troubleshooting and deployment tasks while maintaining human review and quality control.
  • Apply agile delivery practices to shorten development cycles, improve collaboration between business and technical teams, and accelerate time-to-value for AI initiatives.


Who Should Attend

This course is suitable for data scientists, AI engineers, machine learning engineers, data analysts, and IT professionals involved in developing, deploying, or managing AI-driven data science solutions. It is also beneficial for technology managers, solution architects, and digital transformation teams seeking to strengthen their understanding of trustworthy AI, model deployment, and MLOps practices.

Pre-requisites

  • Participants should have prior experience in Python programming and foundational knowledge of machine learning models.
  • Participants should also be comfortable using AI-assisted tools to support coding and analysis.

 

What to Bring

No printed copies of course materials are issued.
Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials.

If you are bringing a laptop, please see below for the tech specs:

 

Minimum

Recommended

Operating Systems

• Windows 7, 8, 10 or
• Mac OS

Laptop running the latest
version of either Windows or
Mac OS

System Type

32-bit

64-bit

Memory

8 GB RAM

16+ GB RAM

Hard Drive

256 GB disk size

 

Others

• An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out)

DirectX 10 graphics card for graphics hardware acceleration

 

 



What Will Be Covered

  • Focus on building trust in AI-driven data science solutions and accelerating project delivery through agile methodologies - Participants will learn about bias mitigation, model explainability, reliability, governance, and business evaluation of AI models, followed by practical discussions on applying agile approaches to AI and data science projects.
  • Model deployment methods and strategies for AI-driven solutions - Participants will explore batch and real-time deployment approaches, model serving infrastructure, scaling and monitoring techniques, as well as safe and iterative model update practices through lectures and practical training.
  • Introduction of MLOps frameworks for deploying scalable and robust AI-driven data science solutions - Participants will learn the core concepts of MLOps, including workflow orchestration and management of complex machine learning pipelines, supported by practical exercises and implementation activities.


Fees & Subsidies

SkillsFuture Singapore (SSG) Funding 2026 (Effective 1 July)

Fee ComponentFull Course FeesSingapore Citizens & PRs Aged 21 Years and Above (70% Funding Support)Singapore Citizens Aged 40 Years and Above (90% Funding Support)Enhanced Training Support for SMEs (ETSS) (90% Funding Support)
Full Course FeeS$2,850.00S$2,850.00S$2,850.00S$2,850.00
SSG Funding-S$1,995.00S$1,995.00S$1,995.00
Nett Course FeeS$2,850.00S$855.00S$855.00S$855.00
9% GST on Nett Course FeeS$256.50S$76.95S$76.95S$76.95
Additional Funding if Eligible Under Various Schemes--S$570.00S$570.00
Total Nett Course Fee Payable, Including GSTS$3,106.50S$931.95S$361.95S$361.95

 

Note:

  1. SSG Funding is available to qualified individuals, subject to meeting the attendance requirement and passing of assessment.
  2. All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
  3. SME fees are applicable only to participants who are sponsored by small and medium enterprises.
  4. SSG funding is subjected to availability.


loading

Certificate

The ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course and pass the required assessments.


Preparing for Your Course

NUS-ISS Course Registration Terms and Conditions

Find out more.

NUS-ISS and Learner’s Commitment and Responsibilities

Find out more.

WIFI Access

WIFI access will be made available to participants.

Venue

NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615

Click HERE for directions to NUS-ISS

In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

Course Confirmation

All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

General Enquiry

Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.



Course Resources

Develop your Career in the Following
Training Roadmap(s)

Please click on the discipline(s) to view the training roadmap of related courses to assess your training needs and goals.

Data Science

Driving business decisions using insights from Data

Read More

You Might be Interested in...

A+
A-
Scrolltop
More than one Google Analytics scripts are registered. Please verify your pages and templates.