How to: Existing ML Systems
Click to expand!
1. [Introducing FBLearner Flow: Facebook’s AI backbone](https://engineering.fb.com/ml-applications/introducing-fblearner-flow-facebook-s-ai-backbone/)
1. [TFX: A TensorFlow-Based Production-Scale Machine Learning Platform](https://dl.acm.org/doi/pdf/10.1145/3097983.3098021?download=true)
1. [Accelerate your ML and Data workflows to production: Flyte](https://flyte.org/)
1. [Getting started with Kubeflow Pipelines](https://cloud.google.com/blog/products/ai-machine-learning/getting-started-kubeflow-pipelines)
1. [Meet Michelangelo: Uber’s Machine Learning Platform](https://www.uber.com/blog/michelangelo-machine-learning-platform/)
1. [Meson: Workflow Orchestration for Netflix Recommendations](https://netflixtechblog.com/meson-workflow-orchestration-for-netflix-recommendations-fc932625c1d9)
1. [What are Azure Machine Learning pipelines?](https://docs.microsoft.com/en-gb/azure/machine-learning/concept-ml-pipelines)
1. [Uber ATG’s Machine Learning Infrastructure for Self-Driving Vehicles](https://eng.uber.com/machine-learning-model-life-cycle-version-control/)
1. [An overview of ML development platforms](https://www.linkedin.com/pulse/overview-ml-development-platforms-louis-dorard/)
1. [Snorkel AI: Putting Data First in ML Development](https://www.snorkel.ai/07-14-2020-snorkel-ai-launch.html)
1. [A Tour of End-to-End Machine Learning Platforms](https://databaseline.tech/a-tour-of-end-to-end-ml-platforms/)
1. [Introducing WhyLabs, a Leap Forward in AI Reliability](https://medium.com/whylabs/introducing-whylabs-5a3b4f37b998)
1. [Project: Ease.ml (ETH Zürich)](https://ds3lab.inf.ethz.ch/easeml.html)
1. [Bodywork: model-training and deployment automation](https://bodywork.readthedocs.io/en/latest/)
1. [Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and more](https://towardsdatascience.com/lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7)
1. [Papers & tech blogs by companies sharing their work on data science & machine learning in production. By Eugen Yan](https://github.com/eugeneyan/applied-ml)
1. [How do different tech companies approach building internal ML platforms? (tweet)](https://twitter.com/EvidentlyAI/status/1420328878585913344)
1. [Declarative Machine Learning Systems](https://dl.acm.org/doi/pdf/10.1145/3475965.3479315)
1. [StreamING Machine Learning Models: How ING Adds Fraud Detection Models at Runtime with Apache Flink](https://www.ververica.com/blog/real-time-fraud-detection-ing-bank-apache-flink)