How deeply does Machine Learning System Design cover reproducibility?

Defines documented and tested regeneration procedures, but stops short of prescribing a particular implementation.

Reproducibility

As explained in Machine Learning System Design

Reproducibility is the property of being able to recreate a dataset or computational result from its original source and documented procedure.

Reproducibility means recreating a dataset from scratch using the same source and a documented software procedure. Replace manually crafted “golden” files with a script, pipeline, or larger system that collaborators can run. section 6.6

A reproducible data workflow should:

Record the origin and preprocessing steps needed to regenerate the dataset. section 6.6
Test the regeneration procedure so every project collaborator can run it. section 6.6
Treat dataset construction like infrastructure as code rather than a one-time manual task. section 6.6
Preserve metadata and ensure the pipeline also remains consistent and available. section 6.8

The missed preprocessing step

A manually assembled dataset may work initially, but a later batch can omit one preprocessing step. Regenerating the data through a tested procedure makes that omission less likely and easier to detect before it affects system performance. section 6.6

Reproducibility supports maintainable dataset updates, while consistency addresses whether labels and other data properties follow stable rules. Both matter when preparing data for machine learning. section 6.6 section 6.8

How Machine Learning System Design covers it

liveBook manning.com

Read Machine Learning System Design on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Designing Deep Learning Systems: A guide for software engineersThe ability to repeat model training with the same inputs and obtain comparable results.
liveBook manning.com

Infrastructure as Code, Patterns and Practices: With examples in Python and TerraformRepeatability is the property of automation that produces consistent results when run multiple times.
liveBook manning.com

Data-Oriented Programming: Reduce software complexityReproducibility is the ability to recreate the behavior of a computation in another environment using the same relevant inputs and conditions.
liveBook manning.com

Delivering Machine Learning Projects: From design to deploymentReproducibility is the ability to recreate a computational result from the same specified inputs, components, and procedures.
liveBook manning.com

Machine Learning Platform EngineeringReproducibility is the ability to obtain consistent results when repeating an operation under the same inputs, configuration, and environment.
liveBook manning.com

Kubernetes for DevelopersReproducibility is the ability to recreate the same application environment from a recorded set of build instructions and inputs.
liveBook manning.com

Architecting an Apache Iceberg LakehouseReproducibility is the ability to obtain consistent analytical or computational results by repeating a process with the same inputs, versions, and conditions.

Compare how 8 books explain reproducibility →

Related concepts

versioningproduced by · The practice of identifying and managing successive revisions of files, data, models, interfaces, or other artifacts so changes can be tracked and earlier states recovered.
random seedconfigured by · A random seed is an initial value or state that initializes or controls a pseudorandom process and determines its subsequent outputs.
snapshotaddressed by · A snapshot is a fixed representation or copy of data or system state as it existed at a particular point in time.
checklistused by · A structured list of tasks, questions, or criteria used to guide an activity or systematically verify that relevant requirements have been addressed.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.