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
Ramesh Sampath | Build Data Apps by Deploying ML Models as API Services
PyCon.DE 2018 LT: Analyzing Twitter Data - Fabian Gebhart
PyCon.DE 2018 LT: Repository Line Length Analysis - Peer Wagner
PyCon.DE 2018: Advanced Analytics Today - Martin Schütz
PyCon.DE 2018: Strongly Typed Datasets In A Weakly Typed World - Marco Neumann
PyCon.DE 2018: Building Your Own Data Science Platform With Python & Docker - Joshua Görner
Hynek Schlawack - How to Write Deployment-friendly Applications - PyCon 2018
Building an agile AI research-to-production experience - GitHub Universe 2018
Deploying Your ML Model
Serving ML Models With FASTAPI, Redis, Kubernetes, Itsio, Grafana, and Consuming API within Flask
PyCon.DE 2018: Cloud Chat Bot For Lazy People - Björn Meier
Machine Learning as a Service
Open Standards for Machine Learning Deployment - Animesh Singh & Hou Gang, IBM
Tutorial - Jules S. Damji: Distributed Python with Ray Hands on with the Ray Core APIs
Kubernetes to serve a fraud detection model - Gabriela Souza de Melo, Legiti
Escape from auto-manual testing with Hypothesis!
PyCon.DE 2018: Germany's Next Topic Model - Thomas Mayer
