Chartered AI Engineer (CAIE)

Learn more about Associate AI Engineer 

Associate AI Engineer

Students or working professionals who have the necessary skills & knowledge to start working on an AI Project. They are not required to display the relevance of their technical skills to business. Rather they are deemed to be proficient in the required technical skills to start working for a business organization that has some data capabilities.

Assessment Rubrics for Associate AI Engineer

1. Programming Proficiency Assessment

  • Code is organized in a clean and readable format
● Code is structured to minimize repeated code (e.g. using Functions)
  • Proper Naming Conventions for variables, functions, classes
  • Include documentation with In-line Comments in source code
  • Intent of Functions and Classes stated clearly
  • Include codes to catch and handle exceptions, such as insufficient data, empty dataset, wrong data type etc

2. Exploratory Data Analysis Assessment

  • Use appropriate visualization tools to generate plots to show the relationship between variables
  • Generate and provide good explanation of descriptive statistics (mean, median, mode, standard deviation, and variance)
  • Include analysis between variables
  • Extract and explain insights from the EDA
  • Link insights into a coherent story
  • Engineer features based on the insights drawn

3. Data Preparation Assessment

  • Perform Missing Data Analysis and take care of missing data appropriately by either removing data records with missing values or perform data imputation
  • Perform Outliers Analysis and take care of outliers appropriately by either removing outliers records or replace outliers data
  • Perform data investigation to check for erroneous data and perform appropriate data preprocessing to correct erroneous data
  • Perform basic transformations for data; e.g.for numerical data, perform mathematical transformations, binning into categories, etc; e.g. for string data, perform string replacement, extract substring, concatenate multiple strings, etc
  • Perform basic feature engineering to improve model accuracy
  • Perform basic train-test-validate data split for model building to ensure the final model is not overfitted and model testing is unbiased

4. Model Design and Development Assessment

  • Display good conceptual understanding of ML algorithms and models
  • Built an appropriate model for the task using major ML framework
  • Evaluate model performance using suitable metrics
  • Able to explain the core concept for model selection, such as the trade-off between Variance vs Bias 
  • Include consideration for a few modeling parameters and architecture for comparison

5. ML Pipeline Setup Assessment

  • Design modular pipeline to ingest data, perform data cleaning, data transformation, train models, generate evaluation metrics and make inference
  • Automate pipeline workflow
  • Functional pipeline that can be can successfully ran end-to-end
  • Include a README file to describe how to run the pipeline
  • Set up ML workflow environment and file structure to facilitate pipeline
  • Include library versioning requirements file, e.g. “” or “conda.yml” depending on the setup option

6. Communication [During Interview]

  • Able to draw insights using EDA
  • Able to collate insights into a coherent story and articulate it clearly
  • Able to justify the methods used for imputing missing values, correcting erroneous data, or handling outliers
  • Able to show how the new features help with model improvement
  • Able to explain the rationale of train-test-validate data split strategy adopted
  • Able to explain how the solution addresses the problem statement
  • Able to clearly articulate the benefits and drawbacks of the ML solution
  • Familiar with how to evaluate the solution submitted
  • Able to identify and address blockers that might arise in the workflow (data curation, model training and inference, deployment)

Register for the Certification

Application fee for Associate AI Engineer