Deep Learning in Production
- Analytics & Tech
- Artificial Intelligence
This programme is conducted on-campus.
2 days
Weekdays (9am - 6pm)
Singapore Management University
Who Should Attend
- AI and Machine Learning Engineers looking to build Gen AI applications
- AI Scientists
- Data Analysts
- Software Engineers
- IT Professionals and Team Leads
Participants are required to have a functional laptop with the following specifications:
- CPU: Must be at least Intel Core i3.
- GPU: Must have an integrated graphics card.
- RAM: Must be at least 4GB.
No prior experience is required. Participants are expected to be proficient in computer usage and possess fundamental logical thinking or numeracy skills. A proactive approach to learning through practical exercises is essential.
Overview
As enterprises increasingly look to operationalise AI, the ability to design scalable, secure, and maintainable deployments becomes critical for achieving business value and regulatory compliance.
Practical exercises cover tools like the LangChain framework, vector databases, and MLOps platforms to monitor and optimise deployed systems. The module also explores challenges such as scalability, reproducibility, and latency, with hands-on activities to address these issues.
The module additionally introduces Generative AI solution patterns - common reusable architectural approaches for integrating LLMs into real-world systems. These include Retrieval-Augmented Generation (RAG), AI agents and agentic workflows, prompt-chaining, multi-modal integration, and guardrails with policy enforcement. Learners will also explore how to deploy Generative AI solutions across cloud, on-premise, and hybrid scenarios. By the end of the module, learners will understand how to deploy, manage, and scale Deep Learning models effectively for business applications.
Learning Objectives
- Deployment Pipeline: Understand the Pipeline paradigm for deploying Deep Learning models
- Model Optimisation: Learn model optimization techniques for increased performance, greater efficiency, and reduced model storage
- Cloud & On-premise Deployment: Learn to deploy Deep Learning solutions to the cloud and on-premise
- Retrieval-Augmented Generation (RAG): Implement Retrieval-Augmented Generation (RAG) for enhancing LLM performance with private data
- Agentic Learning: For autonomous multi-tasking and planning
- Deployment Offerings: Describe and evaluate the differences between various offerings available for deep learning deployment
- Framework & Platform Comparison: Compare and contrast different frameworks, cloud platforms, and options for local deployment
Topic/Structure
- Introduction to Production Pipelines
- Model Optimisation Techniques
- Deploying Models to the Cloud
- Deploying Models On-premise
- Implementing Retrieval-Augmented Generation (RAG)
Assessment
- MCQ
- Written Assignment
As part of the requirement for SkillsFuture Singapore, there will be an assessment conducted at the end of every module. The mode of assessment, which is up to the trainer’s discretion, may be an online quiz, a presentation or based on classroom exercises.
Participants are required to attain a minimum of 75% attendance and pass the associated assessment in order to receive a digital Certificate of Completion issued by Singapore Management University.
Calculate Programme Fee
Fee Table
| EMPLOYER-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$719.40 (After SSG Funding 70%) |
$279.40 (After SSG Funding 70% |
$719.40 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$279.40 (After SSG Funding 70% |
$279.40 (After SSG Funding 70% |
$279.40 (After SSG Funding 70% |
|
International Participant |
$2,398 (No Funding) |
$2,398 (No Funding) |
$2,398 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Employer Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees are eligible for ETSS funding only if their company's SME status is approved prior to the course commencement date. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Employers who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
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