PyCon.DE 2018: How To Deploy ML Models As APIs Without Going Nuts - Anand Chitipothu
How to deploy machine learning models to production (frequently and safely) - PyCon APAC 2018
PyCon.DE 2018: Productionizing Your ML Code Seamlessly - Lauris Jullien
PyCon.DE 2018 LT: Repository Line Length Analysis - Peer Wagner
PyCon.DE 2018: Cloud Chat Bot For Lazy People - Björn Meier
PyCon.DE 2018: Germany's Next Topic Model - Thomas Mayer
Ramesh Sampath | Build Data Apps by Deploying ML Models as API Services
PyCon.DE 2018: Prototyping To Tested Code - Christopher Prohm
Hynek Schlawack - How to Write Deployment-friendly Applications - PyCon 2018
PyCon.DE 2018: Your First NLP Project: Peaks And Pitfalls Of Unstructured Data - Anna Widiger
Machine Learning as a Service
PyCon.DE 2018 LT: Analyzing Twitter Data - Fabian Gebhart
PyCon.DE 2018: Keeping Your Data Secure While Learning From It - Andreas Dewes and Katharine Jarmul
Dr. Tania Allard: Practical DevOps for the busy data scientist | PyData Berlin 2019
Deep learning in production with Keras, Redis, Flask, and Apache
Continuous Deployment for Deep Learning
Deploying Your ML Model
PyCon.DE 2018: Strongly Typed Datasets In A Weakly Typed World - Marco Neumann
Deploying Machine Learning Models in the Browser using TensorFlow.js - Zaid Alyafeai
Serving ML Models With FASTAPI, Redis, Kubernetes, Itsio, Grafana, and Consuming API within Flask
