How to Protect your Python and R Machine Learning Code

What options do you have for distributing Python and R code and still keeping some control over the intellectual property (IP). In this video I look at some of the high level options, such as API’s, Docker Images, Compiled code, and encryption. If there is interest I may follow this up with more technical low-level tutorials on how to protect code in Python. 0:22 What code should you not share? 0:55 Protecting machine learning code 1:16 What are the parts of a model deploy 1:40 Scoring code vs. training code 2:44 What about lookup tables and other data? 3:02 Deploy in the cloud or on the edge? 3:12 Deploying behind an API 4:28 Protecting access to the API 4:50 Authentication and Throttling 5:24 Advsarial Example Attack 6:20 Edge Deployment 7:50 Preventing copying and modification 8:00 Compiled languages 8:23 Does Docker offer protection? 9:50 Protecting Binary files 10:30 What about encryption? Complete Eaglesoft (ESI), C64 Cracking Group Intro Follow Jeff Heat