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awesome-machine-learning-engineering
A curated list of articles, papers and tools for managing the building and deploying of machine learning models, aka machine learning engineering.
https://github.com/d18s/awesome-machine-learning-engineering
- A Few Useful Things to Know about Machine Learning
- Machine Learning glossary
- The Unreasonable Effectiveness of Data
- Revisiting the Unreasonable Effectiveness of Data
- Why you need to improve your training data, and how to do it
- Rules of Machine Learning: Best Practices for ML Engineering
- What’s your ML test score? A rubric for ML production systems
- Machine Learning: The High Interest Credit Card of Technical Debt
- Introducing the Facebook Field Guide to Machine Learning video series
- Patterns for Research in Machine Learning
- Production Data Science
- Making Netflix Machine Learning Algorithms Reliable
- Scaling Knowledge at Airbnb
- Ad Click Prediction: a View from the Trenches
- Learning a Personalized Homepage
- Distributed Time Travel for Feature Generation
- Reliable Machine Learning in the Wild NIPS 2016 workshop
- Reliable Machine Learning in the Wild ICML 2017 workshop
- KDD 2017 Applied Data Science
- KDD 2018 Applied Data Science
- ECMLPKDD 2016 Industrial track
- ECMLPKDD 2017 Applied Data Science track
- ECMLPKDD 2018
- WWW 2018 Industry track
- Command-line Tools can be 235x Faster than your Hadoop Cluster
- Big Data, Small Machine
- Dask
- Unix for poets
- Data Science at the Command Line
- Data hacks
- Split command
- Parallel command
- Xargs command parallel flag
- kubeflow
- ModelDB
- mlflow
- datmo
- Luigi
- Airflow
- Azkaban
- Pinball
- Serving - performance serving system for machine learning models (Google)
- deepdetect
- clipper - latency prediction-serving system (Berkeley)
- MLeap
- openscoring - time scoring (<1 ms) of R, Scikit-Learn and Apache Spark models (openscoring)
- mxnet-model-server
- hydro-serving - Machine Learning Serving cluster (hydrosphere.io)
- Predictive Model Markup Language
- jpmml-sklearn - line application for converting Scikit-Learn pipelines to PMML
- sklearn2pmml - Learn pipelines to PMML
- sklearn-porter - learn estimators to C, Java, JavaScript and others
- Knowledge Repo - generation curated knowledge sharing platform for data scientists and other technical professions.
- Data Pipeline
- Glue
- Simple Workflow
- Batch
- Machine Learning - based service that makes it easy for developers of all skill levels to use machine learning technology"
- Sagemaker
- Dataflow
- ML Engine - learn and XGBoost to the cloud"
- Batch AI
- Machine Learning services
- Machine Learning Studio - and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data"
- awesome-machine-learning
- awesome-etl
- awesome-pipeline
Programming Languages
Keywords
scikit-learn
5
machine-learning
5
data-science
4
tensorflow
3
python
3
xgboost
2
workflow
2
spark
2
deep-learning
2
scheduling
2
artificial-intelligence
1
reproducibility
1
version-control
1
hadoop
1
luigi
1
orchestration-framework
1
azkaban
1
workflow-engine
1
caffe
1
gpu
1
notebook
1
ml
1
minikube
1
kubernetes
1
kubeflow
1
jupyter
1
google-kubernetes-engine
1
scipy
1
pydata
1
pandas
1
numpy
1
dask
1
production
1
collaborative
1
apache-spark
1
api
1
lightgbm
1
pmml
1
r
1
real-time
1
models
1
pipelines
1
realtime
1
scoring
1
serverless
1
serving
1
sklearn
1
data
1
data-analysis
1
knowledge
1