The ability to generate consistent findings utilizing the same input data, procedures, methods, code, and analytic settings is referred to as reproducibility. If something is reproducible in data science and machine learning, multiple individuals or teams can get the same results or conclusions using the same algorithms, tools, and dataset.
So, why is reproducibility necessary? It matters because it ensures the work done is trustworthy, leading to reliable results. Anyone can check the validity of results by using the available data, code, and tools to recreate the process.
Reproducibility benefits research teams because it:
With numerous confusing definitions of reproducibility, it is easy to mistake it for repeatability. However, they are different.
Repeatability pertains to getting the same results under the same conditions—codes, tools, and methods—by the same team or person. If something is repeatable, the developer can reliably repeat their work but still obtain the same outcomes.
On the other hand, reproducibility involves having an independent team or person working with available disclosed resources, including data, codes, tools, and methods, to get consistent results.
Making your projects reproducible is challenging. Adhering to best practices makes achieving reproducibility easier.
Make all your machine learning models reproducible with Pachyderm. It features best-in-class version control and data lineage to keep track of changes at any stage in the ML life cycle easily and efficiently. Try Pachyderm for free today and maintain reproducibility with minimal effort.
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