Python

⚠️ Update Notice (October 2025) Lambda Inference API Deprecation: This post was originally written for Lambda Labs’ Inference API, which was deprecated on September 25, 2025. All code examples have been updated to use the OpenAI API with GPT-4, which provides similar or superior vulnerability detection capabilities. The core concepts, methodologies, and security patterns remain unchanged. Alternative Providers: The patterns demonstrated here work with any OpenAI-compatible API, including: OpenAI (GPT-4, GPT-4-Turbo) Together AI (various open models) Anthropic (Claude models via different SDK) Azure OpenAI Service (enterprise deployments) Research Disclaimer This tutorial is based on: ...

Practical Anomaly Detection using Python and scikit-learn Note: This guide is based on scikit-learn official documentation, academic research on anomaly detection algorithms, and documented best practices from the machine learning community. Code examples are derived from scikit-learn tutorials and tested with scikit-learn 1.3+. Anomaly detection identifies data points, events, or observations that deviate significantly from expected patterns within a dataset. According to scikit-learn documentation, unsupervised anomaly detection is particularly valuable when labeled anomalies are scarce or unavailable—common in cybersecurity intrusion detection, fraud prevention, and system health monitoring. ...

Research Disclaimer This tutorial is based on: PyTorch v2.0+ (official deep learning framework) TensorFlow/Keras v2.15+ (alternative framework examples) scikit-learn v1.3+ (preprocessing and metrics) Academic research on autoencoder-based anomaly detection (Goodfellow et al., 2016; Kingma & Welling, 2013) Production deployment patterns from PyTorch Serve and TensorFlow Serving documentation All implementation patterns follow documented best practices for neural network-based anomaly detection. Code examples are complete, tested implementations suitable for production adaptation. Introduction Looking for classical ML approaches? If you’re new to anomaly detection, start with our guide on classical machine learning techniques using scikit-learn. That post covers Isolation Forest, One-Class SVM, and Local Outlier Factor—excellent choices for tabular data and interpretable results. ...

As I continue this series on CI/CD pipeline security, it is time to now work on securely building and deploying our application. This post picks up where my Build Secure Python Pipelines: Adding Tests and Hooks in Action post left off. In this post, we’ll continue our pipeline development by adding a container build and deployment to Kubernetes. In addition to this, we’ll add some security components to the build and deployment process. ...

As we continue this series started in my Getting Started with Secure CI/CD: Essential Practices for Beginners post, I’ll be securing my Python code with automated testing and hooks. While some of this information builds on some previous posts I’ve created in the past, Adding pre-commit Hooks to Python Repo Writing Tests For Your Python Project I still wanted to incorporate these together in a meaningful way. My goal is to help anyone that is trying to figure out how to piece together their own pipeline. ...