Developers love working with open source, but typically spend too much time in “dependency hell” wrangling packages to work together within their projects. This pain boils over onto DevOps and DevSecOps teams who must ensure security and reproducibility of environments across the SDLC without sacrificing speed.

In this follow-along workshop, we use the ActiveState Platform to set up an example of Python machine learning (ML) runtime with security baked in and consistently applied across operating systems to show how your dev team can get up and running in minutes – pain-free.

Learn how to:

• Automatically build a Python runtime with all required packages built securely from source – right down to C libraries
• Run the Python code immediately for a ML use case using TensorFlow and Flask to identify dogs and/or cats (cute!)
• Deploy that runtime across your team via a command line
• Ensure and validate security end-to-end with CVE remediation, SBOMs and attestations at your fingertips

Workshop Prerequisites:

• Ensure you have Docker installed on your machine and running.
• Pull the docker image to your machine with docker pull ecole5/tensorflow-ml-demo:latest
• Create a free ActiveState account

Key Takeaways:

• Enable Python and other open source language developers (without tedious, painful dependency management) using an ML example
• Secure your software supply chain by automatically building dependencies from source, right down to C libraries
• Ensure security and integrity of open source components throughout your SDLC

Get a Personalized Demo: Book a 30 minute session with our solutions experts to see how ActiveState helps save time, reduce risk and secure your software supply chain.