Chartered AI Engineer (CAIE)

Learn more about AI Engineer Level 1

AI Engineer Level 1

Working professionals who have the necessary technical skills to develop AI solutions. They are able to draw relationship between an AI project and its relevance to business, and design the solution to meet the deliverables.

Assessment Rubrics for Chartered AI Engineer Level 1

1. Project Management Assessment

  • Can clearly articulate how the technical solution links to the stated objective/business need
  • The benefits achieved from the deployment are detailed clearly
  • There is ongoing evaluation of the model performance according to a clear set of metrics
  • Or, if monitoring has not commenced, there is an appreciation of how to enable it
  • Understand who are the end users and able to explain how the AI solution will benefit them
  • Able to estimate the business impact of the AI solution

2. Exploratory Data Analysis Assessment

  • EDA performed with good domain understanding and data insights derived
  • EDA performed resulting in relevant data cleaning or feature engineering
  • 3. Data Preparation Assessment

    • Appropriate choices made in basic pre-processing techniques, for outliers handling, missing data handling, erroneous data correction and other data transformation
    • Feature engineering/ input preprocessing reflects an understanding of the algorithm, with linkages to insights from the EDA
    • Perform train-test-validate data split for model building to ensure the final model is not overfitted and testing is unbiased, with linkages to insights from the EDA
    • Apply sampling method to ensure all classes are well represented in each dataset
    • Ensure there is no data leakage with the train-test split approach used

    4. Model Design and Development Assessment

    • Display good conceptual understanding of ML algorithms and models
    • Adequately and correctly present the underpinning statistical or deep learning framework for the chosen algorithm
    • Adapt current AI research paper or publication to problem statement
    • Evaluate model performance using suitable metrics
    • Able to explain the core concept for model selection, such as the trade off between Variance vs Bias
    • Able to link the metric assessment back to business goal
    • Include a few modeling parameters and architecture for comparison
    • Fine-tuned the model to achieve metrics that are reasonable compared to current state-of-the-art models
    • Include parameter tuning methodology and able to explain the experiments conducted,  as well as draw conclusion from the tuning

    5. Deployment Assessment

    • Address business/operating environment considerations when developing the AI solution
    • Able to describe the high-level architecture
    • Project data is organized and versioned
    • Pipelines are organized in a modularized fashion with reasonable adherence to software engineering best practices
    • Automated/collaborated on the automation of the pipelines for key tasks, including data ingestion, data preparation, model training, metric evaluation, and performance monitoring
    • Solution workflow environment and file structure are setup to facilitate pipeline
    • Understand how to set up consistent infrastructure/platform to support ML development and deployment
    • Able to gauge the need to scale data processing, model training and inference for the ML systems and clarify how to scale where required
    • Able to track system performance of ML deployments (at LEAST ONE identified metrics/KPIs)

    6. AI Governance Assessment

    • Able to explain assessments performed on the fairness of the ML models to mitigate data bias

    7. Communication [During Interview]

    • Able to elaborate the business objectives or goals of the project
    • Able to explain the technical design and implementation of the project to a technical person

    Register for the Certification

    Application fee for AI Engineer Level 1