Software Engineering for Machine Learning
84 items from se-ml/awesome-seml ★1,373
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PyTorch Lightning github.com
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
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Label Studio github.com
A multi-type data labeling and annotation tool with standardized output format.
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Neuraxle github.com
Sklearn-like framework for hyperparameter tuning and AutoML in deep learning projects.
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Seldon Core github.com
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models on Kubernetes.
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Robustness Metrics github.com
Lightweight modules to evaluate the robustness of classification models.
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Model Card Toolkit github.com
Streamlines and automates the generation of model cards; for model documentation.
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Neptune neptune.ai
A lightweight ML experiment tracking, results visualization, and management tool.
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Git Large File System (LFS) git-lfs.github.com
Replaces large files such as datasets with text pointers inside Git.
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MLFlow mlflow.org
Manage the ML lifecycle, including experimentation, deployment, and a central model registry.
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Spark Machine Learning spark.apache.org
Spark’s ML library consisting of common learning algorithms and utilities.
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Software development best practices in a deep learning environment towardsdatascience.com
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ML Ops: Machine Learning as an engineered disciplined towardsdatascience.com
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Tensorflow Extended (TFX) tensorflow.org
An end-to-end platform for deploying production ML pipelines.
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Data management challenges in production machine learning static.googleusercontent.com
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