SITizens Learning Credits


Most AI Deep Learning and data science projects fail, despite promising test results and clear problem statements at the start. This is because many users may start off with unrealistic expectations due to an inadequate understanding of AI; what it can and cannot do, and IT managers may fail to understand the privacy, ethics, cyber security and governance issues related to this new disruptive science. Additionally, there may not be enough high quality data available, and users do not understand the importance of garbage in, garbage out. 

Therefore, the aim of this module is to provide a business deployment perspective of foundational Deep Learning AI capabilities, ranging from Natural Language Processing to Machine Vision; in a manner that requires little or no coding. Neural networks and how Deep Learning works, the data input requirements, project lifecycle and the development framework would be introduced. What problems that each specific AI algorithm can solve would be illustrated in an interactive workshop session and the enterprise benefits would be highlighted via case studies; e.g. enhanced productivity for corporate decision making, process automation or difficult unattended problem solving.

The focus is on rapid prototyping and to guide the users and the relevant project team members to validate project viability using the data and requirements that are available. Importantly, the student will gain the skills to highlight missing gaps, failure scenarios and to discuss AI compliance, privacy and cyber security requirements at the design phase, avoiding misunderstanding of the project’s complexity and hence be able to protect the project from failure.

Finally, modern challenges such as ensuring man-in-the-loop AI oversight, cyber security hardening against adversarial AI attacks and privacy obligations would be covered as new requirements for an upgraded corporate IT risk management framework that will be instrumental in protecting the company’s digitalisation efforts.

The future of AI continues to shine brightly, and we aim to highlight major high value factory automation future possibilities as AI and Big Data systems converge pervasively, and 5G and IoT AI at the edge become commodity. With these insights, the student will master key leadership skills to manage and plan AI projects successfully and hence, deliver strategic benefits to the corporation.

Teaching Team

What You’ll Learn

  • The architecture, applications and operations of popular AI Deep Learning systems and how they would benefit smart factory via enhanced productivity in corporate decision making, process automation and problem solving. Overview of software development using AI frameworks.

  • Overview of Natural Language Processing (NLP) AI systems and key applications.

  • Overview of AI vision processing systems for object recognition, segmentation, pose, biometrics and action recognition.

  • Overview of AI Ethics, governance, AI cyber security vulnerabilities such as Adversarial Attacks, data security, privacy and common implementation weaknesses. Understand privacy and cyber security best practices for AIOps and the need for ethics, transparency and fairness for Trustworthy AI.

  • Overview of AI Project Management and leadership roles.

  • State-of-the-art in AI, future trends and developments; new pending problems that can be solved with better algorithms and the cost reduction expected as the technology advances.

  • Analyse the feasibility of user requirements, data input requirements, AI project life cycle and deployment suitability for Smart Factory

Who Should Attend

  • Engineers and Project Managers of factory and automation systems
  • Professionals with work exposure to manage automation projects or are managing factory innovation deployments, capabilities and associated risks

SITizens Learning Credits (SLC) - Eligible Course

SIT Alumni: Before registering for courses, please activate your SITizens Learning Credits via the email sent by SIT Alumni Team, on behalf of SITLEARN Professional Development.

CLICK HERE TO APPLY (only for SIT Alumni)

Certificate and Assessment

A Certificate of Participation will be issued to participants who:
  • Attend 75% of the course;
  • Undertake non-credit bearing assessment (during course).

A Certificate of Attainment will be issued to participants who:

  • Complete the course and pass all the credit-bearing assessments;
  • Have bachelor degree in engineering or other relevant degree.


Module Date and Timing (9am - 1pm)
1 6 Sep 2021 (Physical Class)
2 - 5 Online (Asynchronous e-learning)
6 Physical/ Zoom Class
7 - 12 Online (Asynchronous e-learning)
Exam 22 Oct 2021


Enhanced Subsidy
Category Full Fee After SF Funding
Singapore Citizen (Below 40) /
Singapore PR
$5,778.00 $1,733.40
Singapore Citizen (40 & above) $5,778.00 $653.40
Non-Singaporeans $5,778.00 Not Eligible
  • All figures include GST. GST applies to individuals and Singapore-registered companies.
  • You can opt for either SkillsFuture Funding or Mid-Career Enhanced Subsidy. Both cannot be combined.

Learn more about funding types available

Terms & Conditions:

SkillsFuture Funding

To be eligible for the 70% training grant awarded, applicants (and/or their sponsoring organisations where applicable) must:
  1. Be a Singaporean Citizen or Singapore Permanent Resident
  2. Not receive any other funding from government sources in respect of the actual grant disbursed for the programme

SkillsFuture Mid-Career Enhanced Subsidy

To be eligible for the 90% enhanced subsidy awarded, applicants (and/or their sponsoring organisations where applicable) must:
  1. Be a Singaporean Citizen
  2. Be at least 40 years old
  3. Not receive any other funding from government sources in respect of the actual grant disbursed for the programme

SIT reserves the right to collect the balance of the programme fees (i.e. the potential grant amount) directly from the applicants (and/or their sponsoring organisations where applicable) should the above requirements not be fulfilled.

SIT reserves the right to make changes to published course information, including dates, times, venues, fees and instructors without prior notice.

Course Series

Find out more about the Modular Certification Courses - Data Engineering and Smart Factory

These modules are stackable towards a Postgraduate Certificate in Data Engineering and Smart Factory (PGCert in DESF) or can be taken individually as a single module as follows:

  • DSF6010 Application of Data Engineering
  • DSF6020 Operational Excellence for Smart Factory
  • DSF6030 Managing and Leading Artifical Intelligence Projects for Smart Factory
  • DSF6040 Application of Robotics & Automation for Smart Factory
Candidates who pass all 4 modules (totalling 24 credits) with a CGPA of ≥ 2.5 will be awarded the Postgraduate Certificate in Data Engineering and Smart Factory by SIT.
SITizens Learning Credits

Key Info

Venue SIT@Dover, 10 Dover Drive S138683/ Online
Time 09:00 AM to 01:00 PM
Date 06 Sep 2021 (Mon) to
22 Oct 2021 (Fri)
Registration is Closed.

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