![]() What’s weird though is that this investment is exactly anti-proportional to the value those models have for the organizations they’re being built for. RapidMiner features like the visual process designer, Auto Model, and Turbo Prep are all designed to make your work more productive in the prototyping phase. Tool vendors and even RapidMiner have also been guilty of supporting this behavior for a very long time. We did some analysis and it looks like less than 1% of all projects get to this stage.ĭata scientists naturally spend of most of their time on the prototyping phase, since that’s where most of the work is and where most of their projects remain. In the operationalization stage, additional technical hurdles are waiting and without proper change management, the organization won’t get much out of the models you’ve built. Of the models that make it to this stage, there are certainly some models that are beneficial and should go into production. We estimate that only 30% of all projects make it to this second phase. Either because there is not enough in the data, or because there was no buy-in. However, most projects will never make it to this phase. You will need buy-in from the stakeholders in the business to make a change. You also perform more feature engineering here, and of course this is also where most of the internal selling takes place. Models are further refined in this phase, often being retrained on larger data sets. Since every project starts there, all projects reach at least this phase. Rather, I’m referring to the various stages of a data science project-prototyping, substantiation, and operationalization.Įvery data science project starts with a prototyping phase where you explore the data, prep it, and build many, many model candidates. I don’t mean the typical 80%-on-data-prep argument here. The first observation is about where data scientists spend most of their time. ![]() The second is how little impact this time has on models in production. The first is how much time we waste on optimizing models. There are two things that still surprise me in data science. ![]()
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