What is AIAP?
AIAP stands for AI Apprenticeship Programme, a 9-month full-time programme that aims to groom local Singaporean AI talents and enhance their career with AI-related skills.
The programme consists of 2 months of deep skilling in AI engineering and seven months of on-the-job training on real-world AI projects from the industry. The programme is open to Singaporeans with technical competency in Python and AI/ML.
Applicants must pass a technical assessment and an interview to be selected for the programme. The technical assessment requires applicants to analyse data and build an end-to-end machine-learning pipeline. The interview will test applicants’ knowledge and skills in statistics, data analytics, machine learning and software engineering.
Why join AIAP?
Some of the benefits of joining AIAP are:
- You will get to work on real-world AI projects with industry partners and gain valuable hands-on experience in deploying AI models into production
- You will receive guidance and mentorship from AISG’s AI engineers and data scientists, who are experts in their fields
- You will receive a monthly training allowance of SGD 3,500 to 5,500, depending on your qualifications and experience.
- You will be part of a community of practice that connects you with other AI professionals and enthusiasts who share your passion and interest.
- You will have the opportunity to obtain AISG and AIP certifications that validate your skills and knowledge in AI.
- You will enhance your career prospects and employability in the growing field of AI
There are many success stories of AIAP graduates who have gone on to work in various sectors and domains using their AI skills and knowledge. Here are some examples:
“There are many technical tutorials out there, but few offer the hands-on experience needed to address real-world problems, and that is one of the key differentiators of AI Singapore’s AI Apprenticeship Programme.”Derek, a data scientist with a government agency.
“AIAP has given me many opportunities to learn from experienced mentors and work on challenging projects. I also appreciate the supportive community of fellow apprentices and alumni who share their insights and resources.”Jia Qi, ML Engineer with a healthcare start-up.
More than 200 AI apprentices have graduated from AIAP so far. The graduates typically receive two or more job offers, and the placement rate is usually more than 90%.
How do I prepare for AIAP?
To prepare for the AIAP technical assessment, you should have a good grasp of the following topics:
- Statistics, data analytics and machine learning concepts and techniques
- Python programming language and its libraries for data manipulation, analysis and visualisation
- Software engineering fundamentals and best practices
- Exploratory data analysis and feature engineering
- Machine learning model development, evaluation and deployment
You should also be familiar with databases, Jupyter notebooks, bash scripts, and other tools commonly used in AI projects.
AI Singapore has provided some resources to help you prepare for the AIAP programme. You can check out the following materials:
AIAP Field Guide (https://connect.aisingapore.org/aiap-field-guide/)
These materials will give you an overview of what to expect from the programme, what skills and knowledge are required, and how to tackle the technical assessment. You can also find some sample questions and solutions to practice your skills.
Joining AIAP can be a rewarding but challenging experience. Some of the challenges that you may face are:
- The programme is highly competitive and selective. Only a small percentage of applicants are accepted into each batch. You must demonstrate your technical skills, knowledge and passion for AI in the technical assessment and the interview.
- The programme is intensive and rigorous. You will need to commit full-time for nine months and work on real-world AI projects with industry partners. You must also learn new concepts, tools and techniques independently and from your mentors and peers.
- The programme is dynamic and uncertain. You will encounter various challenges and uncertainties when working on AI projects, such as data quality issues, changing requirements, technical difficulties, ethical dilemmas and stakeholder expectations. You must be adaptable, resilient and creative in solving these problems.
However, these challenges also present opportunities for you to grow and develop as an AI professional.
Applying for AIAP
Here are some general tips on how to prepare for the application process:
- Review the AIAP website and carefully read the programme details, requirements and FAQs. Ensure you understand what the programme is about, what it expects from you and what you can expect from it.
- Check your eligibility and availability for the programme. You must be a Singaporean citizen with technical competency in Python and AI/ML. You must also be able to commit full-time for nine months (or 12 months if you are 40 years and above) and work on real-world AI projects with industry partners.
- Prepare your resume and portfolio. Highlight your relevant skills, experience and achievements in AI/ML or related fields. Include any projects, publications, certifications or awards demonstrating your passion and proficiency in AI/ML.
- Provide links to your GitHub or other online platforms where you showcase your work.
- Practice your technical skills and knowledge. Review the topics covered in the technical assessment and refresh your understanding of statistics, data analytics, machine learning and software engineering concepts and techniques.
- Practice working with databases, Jupyter notebooks, bash scripts and other tools commonly used in AI projects.
- Prepare for the interview. Research AI Singapore and its programmes, especially the 100 Experiments (100E) programme, where you will work on real-world AI projects with industry partners. Think of some questions you may want to ask about the programme or the organisation. Be ready to explain your motivation for joining AIAP, your career goals and aspirations in AI/ML, your strengths and weaknesses as an AI professional, how you approach problem-solving in AI/ML projects, how you deal with challenges and uncertainties in AI/ML projects, how you work in teams and communicate with stakeholders.