✔ Incremental Development

The natural iterative and incremental problem solving approach of forming, testing and validating hypotheses is present in kaos. Machine learning is similar to any problem with unknown dimensionality, which requires continual improvement towards a desired end goal (i.e. a specific metric or desired KPI).

kaos simulates this process by allowing any number of training inputs - code, data and/or params. A simplified conceptual example is presented below.

Time₀
Time₁
Time₂
Timeₙ
Time₀

Inputs

kaos Command

Code, Data, Params

kaos train deploy -s <code> -d <data>

Resulting model trained with incorrect architecture

Time₁

Inputs

kaos Command

Code with correct architecture

kaos train deploy -s <code>

Resulting model trained with poorly labelled training dataset

Time₂

Inputs

kaos Command

Data with relabelled training data

kaos train deploy -d <data>

Resulting model trained with assumed learning rate

Timeₙ

Inputs

kaos Commands

Hyperparameters with range of learning rates

kaos train deploy -h <hyperparams>

Resulting model satisfies the desired KPI!

incremental model development in time with kaos

kaos enables incremental processing when any or all updated inputs are desired

Check out the Training Pipeline for detailed information regarding its inputs and outputs.