PyCon.DE 2018: Productionizing Your ML Code Seamlessly - Lauris Jullien
PyCon.DE 2018: How To Deploy ML Models As APIs Without Going Nuts - Anand Chitipothu
Lauris Jullien - Productionizing your ML code seamlessly
PyCon.DE 2018: Cloud Chat Bot For Lazy People - Björn Meier
PyCon.DE 2018 LT: Repository Line Length Analysis - Peer Wagner
PyCon.DE 2017 Florian Rhiem - Integrating Jupyter Notebooks into your Infrastructure
PyCon.DE 2018: Keeping Your Data Secure While Learning From It - Andreas Dewes and Katharine Jarmul
PyCon.DE 2018: Strongly Typed Datasets In A Weakly Typed World - Marco Neumann
From a model to production like a Pro: Software-engineering Best-Practices - Marcel Krčah
PyCon.DE 2018 LT: Analyzing Twitter Data - Fabian Gebhart
PyCon.DE 2018: Germany's Next Topic Model - Thomas Mayer
Tutorial: Managing the end-to-end machine learning lifecycle with MLFlow
Talk - Pablo Alcain: Software Development for Machine Learning in Python
Abdulrahman Alfozan - MLFlow | PyData Riyadh
Spark Saturday DC 2017 - Richard Garris - 'Productionizing' ML Models
How Docker supports Machine Learning Models Deployment & Productionization - Use Case
Anatomy of a production ML feature engineering platform - Venkata Pingali
4 Code Quality Tips That Made Me a Better ML Engineer
[Webinar]: How To Productionize ML Models At Scale | Sigmoid
AWS re:Invent 2018: Deep Learning Applications Using PyTorch, Featuring Facebook (AIM402-R1)
![[Webinar]: How To Productionize ML Models At Scale | Sigmoid](https://img.youtube.com/vi/B663q674vcc/0.jpg)
