Due to its experimental nature, analytics development work—including data exploration, experimentation with predictive models, and development of prototypes through rapid iterations— must be performed in a “lab” environment that’s separate from other systems so that it doesn’t hinder normal business operations. Lab technologies must be flexible and scalable to handle the changing demands of the analytical approach (eg, new data, new modeling techniques) and modular to enable developed solutions to port to the factory through DevOps. (View Highlight)
After development in the lab, analytics models move into the “factory” which provides an environment for running analytics jobs 24 hours a day, 7 days a week, 52 weeks a year. In order to put a solution into production at scale (ie, making it regularly and reliably accessible to users), it has to be robust (able to handle typical errors, including variances in incoming real-world data), maintainable, executed efficiently through continuous deployment processes, and integrated with core systems, and it must include performance management and risk controls to avoid any detrimental impact on operations. (View Highlight)
MLOps refers to DevOps as applied to machine learning and artificial intelligence.
Short for “software development” and “IT operations,” DevOps is the application of software engineering practices to IT operations, such as packaging and deploying production software.
MLOps aims to shorten the analytics development life cycle and increase model stability by automating repeatable steps in the workflows of software practitioners (including data engineers and data scientists). While MLOps practices vary significantly they typically involve automating integration (the frequent checking in and testing of code) and deployment (packaging code and using it in a production setting). (View Highlight)
The work in the lab and factory is carried out by cross-functional teams made up of data and software professionals (eg data scientists, machine learning engineers, cloud architects) as well as business professionals with varying levels of data science expertise (eg, subject matter experts, translators). (View Highlight)