AIAP® Technical Assessment

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AI Singapore’s AI Apprenticeship Programme (AIAP®) was launched in 2018 to groom local Singaporean AI talent and enhance their career with AI-related skills. In the first 2 months, apprentices undergo a self-directed deep-skilling phase which is supported by AISG’s AI engineers, data scientists and computational resources. This is followed by a 7-month on-the-job training where AI apprentices execute real world projects with the 100 Experiments team or our in-house engineering teams (see below for the different tracks).

Since its launch, there have been 7 batches of apprentices who have embarked on this award-winning programme. Each AI apprentice will embark on his/her AIAP journey by undertaking the technical assessment first.

This assessment allows applicants to showcase their technical competencies, knowledge and ability to solve machine learning challenges. Basic knowledge in statistics, data analytics, machine learning and software engineering are essential to excel in the programme.

The AIAP team would like to share some pointers that applicants may find useful. In the current iteration of the technical assessment, applicants will have approximately five-and-a-half days to develop an end-to-end machine learning pipeline. This consists of 2 main parts:

  1. Data extraction and analysis
  2. Model development and evaluation

In part 1, applicants are expected to extract the dataset from a database before performing an exploratory data analysis of the dataset (EDA). In the EDA, applicants should develop a good understanding of the dataset by analysing each feature individually as well as their interactions. Applicants should also form hypotheses about the dataset and verify them during the EDA. A good submission involves presenting these findings in a logical and well-thought out process.

In part 2, multiple machine learning models have to be developed and evaluated for the given task. In the development of the models, applicants are required to document their design decisions in data pre-processing and reasoning to support their choice of models. In addition,  models should be evaluated in a meaningful manner with an evaluation metric that is appropriate for the task at hand. When tackling a problem using different approaches, applicants may have to be creative to find a common evaluation metric to ensure consistency across comparisons.

Machine learning is more than training models! 

[Image adapted from https://tdan.com/making-crisp-dm-work-for-embedded-analytics/25884]

As part of the submission process, applicants will be required to package their work for review. Submissions should include an iPython notebook, Python and bash scripts as well as any files required to recreate the development environment. Reproducibility is an extremely important tenet in machine learning and the ability to reproduce an applicant’s results is a key assessment point. Submissions should also demonstrate an understanding of software engineering fundamentals and all code submitted should incorporate proper coding conventions while being readable and easily understood.

We do expect applicants to demonstrate a reasonable understanding of key concepts in statistics, data analysis and machine learning. While heuristics, checklists and flow charts for standard practices may be useful, AIAP applicants are expected to understand the rationale behind these tools and be able to decide (and justify) how to apply these ideas in new situations.

Finally, as the development of machine learning models is an iterative process with a considerable amount of experimentation, it is recommended that applicants take a principled approach to experimentation and appropriately justify any design considerations.

We are heartened to see the improvement in the quality of submissions in each batch’s application and we hope that the pointers above will be helpful to current and prospective applicants.  We would like to wish all applicants the very best of luck in their assessment and we look forward to welcoming you as AI apprentices.

Here are some projects past apprentices have worked on:

Author

  • Daniel is currently leading the Computer Vision Hub and AIAP. He has interest in expanding the usage of computer vision for different business use cases.

  • Benedict is an AI Engineer with AI Singapore and is keen on Machine Learning and Natural Language Processing.

  • AI Engineer at AI Singapore

  • AI Engineer with a background in Information Systems. Currently an engineer at cvhubs in AI Singapore.

  • Enjoys solving problems with the use of technology. Currently in the SecureAI team, exploring tools for testing the robustness of machine learning models and incorporating best practices into the ML development process.

  • AI Engineer @ AISG, CV Hub.

  • Sidney is an AI Engineer at AI Singapore and is ever-aspiring to become a Data Unicorn. Having studied from Psychology, he has honed his mind-reading skills to read an AI Agent's mind.

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