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Back of the envelope calculation for machine learning projects


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Dr. Larysa Visengeriyeva
Dr. Larysa Visengeriyeva

Worum geht’s?

One of the best practices that we know from great engineers is the back-of-the-envelope calculation to estimate costs and resources. I believe that in Machine Learning Engineering, we all would benefit from such a “back-of-the-envelope calculation” skill to create a prototype of an ML Project. We need to confirm - as cheaply as possible - that our future ML project is worthwhile, that it will solve my business problem, and that costs and resources are feasible. In my talk, I suggest a design toolkit for ML projects to perform such rough prototyping by using three canvases: Machine Learning Canvas, Data Landscape Canvas, and MLOps Stack Canvas.

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