MLOps: 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 access to detailed data. 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.

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. Detailed access to 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. Last but not least, the best feature engineering platforms automatically convert feature declarations into scalable and optimized SQL or Spark code. 

For more information about feature engineering platforms, click here.