AI APPRENTICESHIP PROGRAMME®
Programme Structure
AIAP 9-Month 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.
PROMPT ENGINEERING
Apprentices assigned Prompt Engineering projects will be equipped with in-depth knowledge and skills in Prompt Engineering, which is an emerging field in AI that specializes in creating and refining prompts, to effectively leverage the remarkable capabilities of Large Language Models to complete a diverse range of tasks.
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.
Requirements
Who Can Apply
- Singapore Citizens only
- NITEC, Diploma or Degree holders from a recognised Institute of Higher Learning (IHL)
- Eligible for TeSA CLT Funding
Knowledge and Proficiency 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 databases 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
