The AI Singapore’s AI Apprenticeship Programme (AIAP)® was created to meet AI Singapore’s requirements for AI Engineers. It grew out of a need for a core group of Singaporean AI talents working in AI Singapore, solving both Singapore’s and Singapore’s companies’ problem statements with AI.

While we hire from the open market, we could not find sufficient Singaporean AI talents, so we created our programme – the AIAP – to identify, train and groom these talents.

We focused on self-directed, passionate Singaporeans with some of the required AI technical knowledge and skills but may not have deployed AI models into production. We felt these Singaporeans could be offered a 2-month intensive deep-skilling training and then be put into a 7-month real-world AI 100E project.

Today, AIAP is recognised as the leading AI deep-skilling programme in Singapore and the world. Many organisations and countries are learning from us and are trying to replicate our AIAP + 100E model. So, join us and experience the AIAP way.

application for AIAP Batch 12 is Now closed!

AIAP Batch 13 application Opens in November 2022.

Please sign up for our mailing list to be kept updated about the AI Apprenticeship Programme. You may also wish to join our AI community over at The Epoch! And those who need some learning resources, head to LearnAI or use the AIAP Field Guide to focus your pre-AIAP learning.

Accepting applications for AIAP Batch 13!

Closing date: 18 December 2022

Timeline for AIAP Batch 13

Application Period

17 November – 18 December 2022

Technical Assessment

11 – 16 January 2023

Invitation to Interview

3 February 2023

Interview

11 February 2023

Offer

3 March 2023

Programme Period

8 May 2023 – 9 February 2024

Programme Period (Matured Mid Career)

8 May 2023 – 10 May 2024

Real-world AI

Going beyond Jupyter notebooks to deploy real-world aI systems

The AI Apprenticeship Programme (AIAP)® seeks to develop Singaporean AI talents and enhance career opportunities in AI-related roles.

Apprentices will get to work on industry projects and deepen their skills in AI and machine learning (ML), and software engineering as they learn to deploy AI models into production.

Commitment

Full Time

AIAP is a full-time programme. Apprentices will learn and work in AISG’s office Monday-Friday, 9 am – 6 pm.

Duration

2 + 7 months

2 months of deepskilling in AI Engineering and 7 months of on-the-job training on a real-world AI problem from the industry.

Competency

Python & AI/ML

Candidates must have technical competency with Python and AI/ML. See competency requirements here.

Stipends

$3.5k-$5.5k

A monthly training allowance of SGD 3,500 to 5,500 will be provided (dependent on no. of years of relevant working experience and qualifications).

The AIAP Life

Programme Structure

AIAP 9 Months Programme Structure

Apprentices will benefit from a range of content to gain exposure to machine learning concepts, develop career and technical assessment skills and ultimately, instil confidence to solve problems and handle the unknown. ​

Curriculum

Part 1- Machine Learning Fundamentals
Perform thorough exploratory data analysis and ability to utilise traditional machine learning models to solve AI/ML problems  

Topics covered:

  • Learn how to acquire and clean data for AI/ML workloads
  • Ability to clean and pre-process data appropriately for ML models
  • Learn how to conduct thorough exploratory data analysis
  • Learn how to feature engineer for AI/ML models
  • Understand the fundamentals behind the different clustering models and when to apply such models
  • Ability to utilise dimensionality reduction techniques

Part 2 – Advanced Courses in Machine and Deep Learning
Use neural networks and deep learning techniques to solve problems

Topics covered:

  • Understand the fundamentals of neural networks
  • Learn the process and methods for computer vision tasks
  • Learn the process and methods for natural language processing tasks   

Part 3 – Deployment
Containerise and deploy an AI/ML model         
(Note: Trainees will have to submit Python scripts and demonstrate a web application)    

Topics covered:

  • Understand fundamentals of application deployment
  • Understand how to utilise tests appropriately
  • Learn how to maintain proper and clear documentation
Self-directed Learning

Learning is self-directed. Every week, apprentices tackle individual assignments, explore existing solutions and contribute to extensive discussions with mentors and field experts.​

Tailored Learning Discussion

We tailor discussions to extend an apprentice’s knowledge, challenge them to think more deeply and impart the thinking and research skills essential to remaining relevant.​

100 Experiments

In 100E projects, apprentices help companies to solve real-world business problems using AI. These companies span industries such as education, engineering, FMCG, healthcare, insurance, maritime and travel, as well as government agencies. You will build minimum viable products consisting of AI models and end-to-end pipelines which will be deployed into production. 100E projects allow you to experience what it is like to work with clients and stakeholders while developing applications that cover a wide range of AI technologies in computer vision, natural language processing and general ML.

AI Bricks (Computer Vision, NLP, RPA etc.)

Computer Vision Hub projects give apprentices the opportunity to solve real-world problems using cutting-edge computer vision models for pose estimation, object detection, image segmentation and activity recognition. You will learn to combine these models and algorithms to develop unique computer vision solutions.

The NLP Hub exposes apprentices to common NLP problems that industries face and ways of addressing them through end-to-end solutions that go beyond model deployment. You will learn how to solve problems such as question answering and summarisation and package them into open-source solutions for industry and the community to use.

AI Bricks are plug-and-play ML tools that allow users to learn, build and deploy simple ML solutions. You will have the opportunity to develop AI Bricks based on published papers and source code from leading AI researchers in Singapore as well as feedback from the industry and create usable solutions and products for businesses.

Data Engineering

Through data engineering projects, you will learn how to develop a secure data management platform to support rapid iteration of ML experiments and enable the results to be reproduced. In addition to traditional data management features such as security and availability, the platform will enable data onboarding, discovery, data governance as well as integration with ML frameworks.

AI Platforms

You will learn to build robust AI and ML applications using modern infrastructure and tools. Aligned with AI/ML best practices, these platforms empower researchers, engineers and collaborators to work together on projects such as the development of AI Bricks, and to solve challenging business problems.

Federated Learning

You will have the opportunity to work on Synergos, a federated learning system platform developed by AI Singapore. The Synergos platform enables organisations to train a machine learning model collaboratively without exposing data to one another. This helps to address the privacy concern associated with explicit data sharing.

SecureAI

SecureAI lies at the intersection of ML, systems and security. You will work with experts from the fields of AI, ML, adversarial learning methods and computer systems security to identify risks and improve the safety, robustness and trustworthiness of AI systems.

AI Standards

You will get to work on an audit framework that allows Chartered AI Engineers (CAIE) to confidently sign off on AI projects. Leveraging industry best practices and existing open source frameworks, the audit will assess AI projects from an end-to-end perspective encompassing data, modelling, deployment and ethics and governance, and lead to the AI Trust Mark (AITM) award.

Requirements

Minimum Requirements

  • Singaporean
  • Graduated from a recognised University or Polytechnic (Poly grads will need to have at least 3 years of working experience)
  • Eligible for TeSA CLT / TMCA Funding

Competency requirements

  • Intermediate programming experience in one of these languages: Python, R, Scala, Java, C, C++, C#, Go.
  • Understand basic data pre-processing (handling missing data, outliers etc…)
  • Able to build machine learning models
  • Able to build data pipelines to train and build your models
  • Able to perform basic code documentation (Readme, docstrings and requirements.txt)
  • Able to deploy your models in Docker containers
  • Able to provision and use cloud computing infrastructure such as Google Cloud, Microsoft Azure or AWS.
  • Able to do Linux shell scripting
  • Able to use at least one of the following database and data processing technologies such as SQL, NoSQL, Apache Hadoop and/or Apache Spark
  • Able to use GitHub/GitLab and perform proper code check-in/out and repository

Becoming an AI Apprentice

All candidates will be invited to the technical assessment via their registered email 

The technical assessment consists of two parts:

  • Exploratory Data Analysis
  • End-to-end machine learning pipeline

You are to complete the above-mentioned tasks based on a given problem statement within a given time frame. Your exploratory data analysis is to be performed in a Jupyter notebook (.ipynb file format). Your end-to-end machine learning pipeline is to be developed in Python scripts (.py file format).

Your submission will be assessed in the following four areas:

1. Appropriate data processing methods

2. Insightful exploratory data analysis

3. Appropriate model development pipeline

4. Programming proficiency

If you pass the technical assessment, you will be shortlisted to proceed to interview stage. This will involve a Technical presentation & Group exercise Please note: Shortlisted candidates are expected to present their technical solution which was submitted for the technical assessment.

The interview stage consists of two parts.

In the first part, you are given 10 minutes to present your technical assessment submission. The content of the presentation is decided by you. You can present directly from your submission (README, Jupyter Notebook) or prepare slides (PowerPoint, Google Slides).

The second part of the interview requires you to work on a case study together with 1 – 2 other candidates. The case study is only disclosed on that day itself.

Tips from mentors and apprentices

Tips from AI Mentors and Apprentices

Companies which Have hired our Apprentices

Ready to Become an

AI Apprentice?

Please sign up for our mailing list to be kept updated about the AI Apprenticeship Programme. You may also wish to join our AI community over at The Epoch! And those who need some learning resources, head over to LearnAI or use the AIAP Field Guide to focus your pre-AIAP learning.

Move beyond books and MOOCs, and gain real-world AI experience in the award-winning AI Apprenticeship Programme.