Any cycle of continuous integration or delivery can benefit from reproducibility. It makes it possible for these processes to run without hiccups, making client-facing modifications and deployments less of a hassle and more of a habit for everyone involved.
The ability to reliably reproduce results allows your team to cut down on mistakes and uncertainty as they transition from design to implementation. It might be difficult to maintain consistency in data for a machine learning project if researchers cannot be convinced that the findings they have obtained are accurate.
- A scalable and repeatable ML application might also help your company expand. The effort put into ensuring the pipeline is properly architected and programmed will help to accommodate the hopefully increasing need for rapid and massive model execution.
Credibility and confidence in the ML product are established with reproducibility. An ML pipeline is a method of communicating the guarantees associated with a well-planned, developed, and released ML project.
From the very beginning of a machine learning project, data reproducibility needs to be top of mind. It needs to be integrated into the whole project. This demands a positive attitude towards repeatability throughout the board.
- To guarantee projects can be replicated, documentation must begin on day one.
For the project to have reproducible data, the documentation process must include an explanation behind each decision taken. It should keep tabs on the theories, experiments, and results that are being considered.
The team as a whole should have a pipeline approach when it comes to building and releasing. This doesn’t always mean a unified codebase is used for every iteration of a hypothesis or production model. It is often organized as a series of modules of code that carry out the function of a typical model development or production scoring phase such as data gathering, feature reduction, adjusting candidate models, and scoring. The result of one process becomes the input of that which follows. There is no universally accepted pipelining framework or methodology; instead, pick one that works best in your specific setting.
The result is a transparent and repeatable machine learning application built on a codebase that can be easily changed for production, subjected to rigorous testing and debugging, and scaled effectively.