The time has come to apply the principles of DevOps to data science.
Let's explore the concepts of data science and software engineering principles with Head Of Engineering and Machine Learning, Joerg Schad. Check out Joerg's talk from Data Con LA, we can't wait for this year!
Towards Data Science Engineering Principles
Over the last half century we have developed and refined the discipline of software engineering in order to accelerate the development and deployment of applications. This has involved a general shift towards DevOps practices that align developer and business objectives and dramatically reduce time-to-delivery.
With the recent rise of data science and data analytics, the time has come to apply the principles of DevOps to data science and leverage the lessons from software engineering (and its systematic and repeatable methodology) to the discipline of data science. This rapidly emerging field is sometimes referred to as DataOps, and encompasses development of AI models and the overall platform surrounding them. In order to explore this concept, let's compare and contrast data science and software engineering principles. We can uncover similarities and differences between the two across the application development lifecycle.