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What problems does Vevesta solve?

• Hard to identify and discover features (inputs to the ML model):
  As data scientists, it takes them 10 days to find the right features for their models. Many Data Scientists solve this problem by talking to domain experts or searching the database for features. Then finally writing from scratch the SQL queries to extract the data. It was then, it struck that if data scientists had access to input features and features engineered by their fellow data scientists. They can reutilize the features and the corresponding code as inputs to their own models.
• Time-consuming search for techniques/ML algorithms:
  As data scientists, it is painful for them to search for new techniques relevant to the problem at hand. Say, I might be a Computer Vision specialist, and suddenly I am asked to work on a forecasting problem. The pain of searching over the web for solutions and then learning them and finally coding them up is extreme. Vevesta realized if data scientists/ML engineers could leverage the techniques as well as the code used by their peers to work on similar problems, data scientists will get a breather in their projects.