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Awesome Software Engineering for Machine Learning
A curated list of articles that cover the software engineering best practices for building machine learning applications.
Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.
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Deployment and Operation
A booklet on machine learning systems design with exercises
Reproducible Rapid Research for Neural Architecture Search (NAS)
Always know what to expect from your data.
A thoughtful approach to hyperparameter management.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.
a tool that leverages rich metadata and lineage information in MLMD to build a model card
A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.
Airflow is a platform to programmatically author, schedule and monitor workflows.
Robust visualizations to aid in understanding machine learning datasets.
Replaces large files such as datasets with text pointers inside Git.
A platform for data scientists who want to build and experiment with ML pipelines.
platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework anf language agnostic, take a look at all the built-in integrations.
Experiment tracking tool bringing organization and collaboration to data science projects.
An inclusive movement to build an open, organized, online ecosystem for machine learning.
An end-to-end platform for deploying production ML pipelines.