>the hardest part isn’t: “We need to increase our accuracy by 2% by using Restricted Convolutionallly Recurrent Bayesian Machines!” The hardest part is convincing people you need to integrate a new process into a “production” workflow, and then maintaining that process.
Completely agreed. My timeline for a project usually goes like:
A. 2-4 weeks: deeply understand the problem, talk to stakeholders, gather requirements, plan out the project.
B. 2 weeks: explore the problem and the data. Build and tweak models, build a functional prototype.
C. 8-24 weeks: put the system into production on top of the companies' tech stack, either myself or working closely with engineers.
D. 4-12 weeks: sell the system internally, prove that it's a superior solution, get buy-in that it should replace existing processes.
So yeah, in a typical 6 month project I only spend about 5-10% of my time on actual data and modeling. This % has gone sharply down as my career has progressed.
Completely agreed. My timeline for a project usually goes like:
A. 2-4 weeks: deeply understand the problem, talk to stakeholders, gather requirements, plan out the project.
B. 2 weeks: explore the problem and the data. Build and tweak models, build a functional prototype.
C. 8-24 weeks: put the system into production on top of the companies' tech stack, either myself or working closely with engineers.
D. 4-12 weeks: sell the system internally, prove that it's a superior solution, get buy-in that it should replace existing processes.
So yeah, in a typical 6 month project I only spend about 5-10% of my time on actual data and modeling. This % has gone sharply down as my career has progressed.