A curated list of articles, papers and tools for managing the building and deploying of machine learning models, aka machine learning engineering.
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A curated list of articles, papers and tools for managing the building and deploying of machine learning models, aka machine learning engineering.

Where to start


Best practice

Example pipelines

Conference tracks and workshops

Big data on a single machine / on the command line


Managing building and deploying models

  • kubeflow Machine Learning Toolkit for Kubernetes (kubeflow)
  • ModelDB A system to manage machine learning models (MIT)
  • mlflow Open source platform for the complete machine learning lifecycle (Databricks)
  • datmo Open source model tracking tool for data scientists

Managing building models

  • Luigi is a Python module that helps you build complex pipelines of batch jobs. (Spotify)
  • Airflow is a platform to programmatically author, schedule, and monitor workflows (Netflix)
  • Azkaban workflow manager (LinkedIn)
  • Pinball is a scalable workflow manager (pinterest)

Deploying models

  • Serving A flexible, high-performance serving system for machine learning models (Google)
  • deepdetect Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow, XGBoost and TSNE (deepdetect)
  • clipper A low-latency prediction-serving system (Berkeley)
  • MLeap Deploy Spark Pipelines to Production (combust.ml)
  • openscoring REST web service for the true real-time scoring (<1 ms) of R, Scikit-Learn and Apache Spark models (openscoring)
  • mxnet-model-server Model Server for Apache MXNet is a tool for serving neural net models for inference (AWS)
  • hydro-serving ML FaaS - Machine Learning Serving cluster (hydrosphere.io)

Serialising and transpiling models

Monitoring models

  • Knowledge Repo A next-generation curated knowledge sharing platform for data scientists and other technical professions.


  • Data Pipeline "is a web service that you can use to automate the movement and transformation of data"
  • Glue "is a fully managed ETL (extract, transform, and load) service"
  • Simple Workflow "makes it easy to build applications that coordinate work across distributed components"
  • Batch "enables you to run batch computing workloads on the AWS Cloud"
  • Machine Learning "cloud-based service that makes it easy for developers of all skill levels to use machine learning technology"
  • Sagemaker "is a fully managed machine learning service"

Google Cloud

  • Dataflow "is a unified programming model and a managed service for developing and executing a wide variety of data processing patterns"
  • ML Engine "brings the power and flexibility of TensorFlow, scikit-learn and XGBoost to the cloud"


  • Batch AI "helps you experiment with your AI models using any framework and then train them at scale across GPU and CPU clusters"
  • Machine Learning services "enable building, deploying, and managing machine learning and AI models using any Python tools and libraries"
  • Machine Learning Studio "is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data"

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