Data Scientists Need Self-Service

Data scientists require self-service capabilities for many reasons, but primarily, they need the ability to experiment rapidly and iterate quickly on their ideas. This means having the freedom to try out different approaches and techniques without unnecessary delays or dependencies. Feature engineering, a crucial aspect of data science, relies on a deep understanding of the problem domain and data, and access to detailed data for analysis. Data scientists need the freedom to define and test data transformations on this detailed data, without having to depend on data engineers to recode their ideas into SQL or Spark. And they can certainly use help in developing an understanding of the data and domain, without having to rely on domain experts and data owners that are difficult to track down.

One crucial aspect of a data scientist’s work is feature engineering, which involves creating new variables or transforming existing ones to improve model performance. The process requires data scientists to draw upon their intuition and experience to ideate variables that would be relevant to a business problem. Access to detailed data is essential for effective feature engineering. Self-service allows data scientists to directly access the required data, enabling them to define and test data transformations efficiently.

However, there are challenges associated with self-service in data science. The use of non-standard feature engineering code can also limit the reusability and interpretability of features. While data scientists are highly qualified to write complex feature engineering code, they are still only human and can make mistakes. Furthermore, data scientists may lack the database optimization knowledge of specialist data engineers, resulting in issues with code efficiency, speed, and scalability. Similar to software engineering, self-service feature engineering requires governance to ensure version control and maintain consistency.

Data scientists often prefer to work with a local copy of historical data for experimentation purposes. However, moving large amounts of data can be inefficient and may pose privacy risks.

Modern feature platforms have evolved to address these challenges. It is possible to implement guardrails to prevent human error and mitigate the risks associated with self-service feature engineering. Data movement is minimized with declarative feature engineering, resulting in an efficient and secure process. Intelligent self-organizing feature catalogs reduce redundancy and promote efficiency by identifying duplicate features. AI systems are kept up-to-date by version control guardrails that identify obsolete features. Feature declarations can be automatically converted into scalable and optimized SQL or Spark code, speeding up historical data generation and production pipeline deployments. Last but not least, the best feature platforms can automatically understand data and assist data scientists in ideating features for a business problem. 

FeatureByte is designed with the data scientist in mind, acting as a copilot throughout the entire feature engineering lifecycle. Data scientists can automatically ideate use case-specific features, create their own custom features with just a few lines of code, experiment with detailed data, deploy pipelines in an instant, and manage their features in production with a self-organizing catalog. 

Try FeatureByte free for 14 days to experience the power of an intuitive, powerful copilot for AI data.