Best Practices for Machine Learning Applications

Data management challenges in production machine learning

How to organize data labelling for ML

The curse of big data labeling and three ways to solve it

The ultimate guide to data labeling for ML

10 Best Practices for Deep Learning

How do you manage your Machine Learning Experiments?

Nitpicking Machine Learning Technical Debt

Preparing and Architecting for Machine Learning

Software development best practices in a deep learning environment

Testing and Debugging in Machine Learning

Best Practices in Machine Learning Infrastructure

Machine Learning Logistics

Machine learning: Moving from experiments to production

ML Ops: Machine Learning as an engineered disciplined

ModelOps: Cloud-based lifecycle management for reliable and trusted AI

Operational Machine Learning

Machine Learning Interviews

Managing Machine Learning Projects

Principled Machine Learning: Practices and Tools for Efficient Collaboration

Beyond Debiasing

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Aim

Aim is an open source experiment tracking tool.

Airflow

Programmatically author, schedule and monitor workflows.

Alibi Detect

Python library focused on outlier, adversarial and drift detection.

Archai

Neural architecture search.

Data Version Control (DVC)

DVC is a data and ML experiments management tool.

Facets Overview / Facets Dive

Robust visualizations to aid in understanding machine learning datasets.

FairLearn

A toolkit to assess and improve the fairness of machine learning models.

Git Large File System (LFS)

Replaces large files such as datasets with text pointers inside Git.

Great Expectations

Data validation and testing with integration in pipelines.

HParams

A thoughtful approach to configuration management for machine learning projects.

Kubeflow

A platform for data scientists who want to build and experiment with ML pipelines.

Label Studio

A multi-type data labeling and annotation tool with standardized output format.

LiFT

Linkedin fairness toolkit.

MLFlow

Manage the ML lifecycle, including experimentation, deployment, and a central model registry.

Model Card Toolkit

Streamlines and automates the generation of model cards; for model documentation.

Neptune.ai

Experiment tracking tool bringing organization and collaboration to data science projects.

Neuraxle

Sklearn-like framework for hyperparameter tuning and AutoML in deep learning projects.

OpenML

An inclusive movement to build an open, organized, online ecosystem for machine learning.

PyTorch Lightning

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

REVISE: REvealing VIsual biaSEs

Automatically detect bias in visual data sets.

Robustness Metrics

Lightweight modules to evaluate the robustness of classification models.

Seldon Core

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models on Kubernetes.

Spark Machine Learning

Spark’s ML library consisting of common learning algorithms and utilities.

TensorBoard

TensorFlow's Visualization Toolkit.

Tensorflow Extended (TFX)

An end-to-end platform for deploying production ML pipelines.

Tensorflow Data Validation (TFDV)

Library for exploring and validating machine learning data. Similar to Great Expectations, but for Tensorflow data.

Weights & Biases

Experiment tracking, model optimization, and dataset versioning.