I am currently preparing for ML Engineer interviews and wanted to learn a design methodology to crack the design rounds. I have looked for standard resources but couldn’t find any. So I thought of coming up with one on my own from my own experiences and material that I have read online. So below is an overview of the approach I have in my mind. Please let me know what you think of it.

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1. Understand the use case.
   1. Figure out what can be solved deterministically
   2. Figure out what needs to be solved using ‘Data + Machine learning’
2. Pose the problem(1.b) as a math problem.
   1. Come up with an objective that you want to optimize.
3. Select the right data set.
   1. What data is needed. How is the labeling done/derived?
   2. How do you deal with bias, skewed classes,
4. Feature Engineering.
   1. Think about what(information) you need to solve the problem optimally.
   2. What aspects from the data could you use to replicate findings in 4.a?
   3. Transform data into features and a form more apt for model’s learning
5. Model selection:
   1. What model is best suited to solve the problem(2.a) at hand.
   2. Are there any off the shelf(direct or transfer learning) that would help.
6. Training:
   1. How will you train
   2. How will you handle: skewed classes and other problems associated with the dataset.
   3. Validation: what metrics, experiments do you conduct to validate the model’s learning.
7. Productionizing:
   1. How will the solution be deployed?
   2. Performance monitoring, feedback loop/retrain,
   3. Application scaling and model maintenance.