A Simple 3-Step Framework for Machine Learning Workflows

It’s one thing to build a platform tool that integrates Machine Learning workflows, but it’s another to build it for non-tech run-of-the-mill, people-like-you-and-me employees to navigate and use. Here at AI Singapore (AISG), we want to embrace an inclusive design for Machine Learning Platforms such that any business or startup can not only use AI products, but create their own AI-operated products (e.g. creating a chatbot).

Over the course of several AI Industry led projects, our lean design team at AISG have come up with a simple 3-step breakdown of the average ML process/workflow. Note that this is meant to be a flexible, malleable, adaptable framework, with the goal being to try and simplify what seems to be an unnecessarily convoluted series of steps, into the following:

  1. Onboarding
  2. Model Training
  3. Results & Evaluation

Be it Natural Language Processing (NLP) or Computer Vision, this 3-step flow can be applied to most branches of Machine Learning. 


1. Onboarding

The first step of every ML-workflow is onboarding, which gives the user a debut of how the platform looks visually, as well as what’s expected of[from] them to kick things off. In most cases, that would be introducing the task of uploading a required dataset, file, image, or video, to get started.


Formatting the Dataset

Since a set of training data can consist of anything from a collection of text, to images, videos, and the like, it is important to communicate clearly to the user what data the model needs, and what specific format it should be in.

The user needs to understand how to prepare and organize the data for upload. You can use a checklist to simplify the requirements for the user:


Dataset formatting checklist for Natural Language Processing

  • .csv format

  • 2 columns of data only

  • first row contains headers (e.g. “Title”)

  • subsequent rows (second and onwards) contain cells filled with different “groups”* of knowledge data respectively

    *If your knowledge data is not yet grouped/categorize, please refer to this document on how to do so

Other Onboarding tips:

  • Are there any other inputs needed from the user?
  • Are there any other decisions the user needs to make?


2. Model Training

Once the required dataset has been uploaded, and the conditions are set for the model to begin running, the user needs to have a basic understanding of how to fine-tune the model once it starts the training.

It is extremely important that users know how to evaluate the output to assess whether it is addressing the target user’s needs.


  • Does the chatbot model answer the question with the right data?
  • Does the CV model match the right face to the right person?


3. Results & Evaluation & Deployment

Once the user has made their own adjustments to try and improve the model, it is important to give them relevant statistics so they can make an informed decision to temporarily stop training the model by determining the quality of results. In most cases, a Confidence Level is used.

Please explain clearly and succinctly to the user what that is and how to interpret it.

If you’re intending to display other forms of data visualizations like pie charts and matrices, ensure there is sufficient explanation and visual guides on how to interpret such different forms of data.



Once you’ve familiarized yourself with this 3-step process, bear in mind that it is actually a loop with probably more actions in between each step, that looks something like this: