https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/tags/ai/Recent content in AI onHugo -- gohugo.ioen-USMon, 30 Mar 2026 00:00:00 +0000https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/chainguard/migration/the-guardener/Mon, 30 Mar 2026 00:00:00 +0000https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/chainguard/migration/the-guardener/The Guardener migrates your Dockerfiles to use Chainguard Containers. It uses AI to iteratively convert instructions, build images, compare results, and fix issues until the Dockerfile works as expected. You interact with it through chainctl agent dockerfile commands. The AI runs server-side and scans your workspace to perform its analysis. Docker builds and file access remain local to your machine, and only the data necessary for analysis is processed. Note: The Guardener is currently in beta.https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/chainguard/chainguard-images/about/beyond_zero_pytorch_2024/Sat, 07 Sep 2024 01:21:01 +0000https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/chainguard/chainguard-images/about/beyond_zero_pytorch_2024/Recording of Beyond Zero: Eliminating Vulnerabilities in PyTorch Container Images presented by Dan Fernandez, Srishti Hegde, and Patrick Smyth at PyTorch 2024 Session Description Container images are increasingly the future of production applications at scale, providing reproducibility, robustness, and transparency. As PyTorch images get deployed to production, however, security becomes a major concern. PyTorch has a large attack surface, and building secure PyTorch images can be a challenge. Currently, the official PyTorch runtime container image has 1 CVE (known vulnerabilities) rated critical and 5 CVEs rated high.https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/chainguard/chainguard-images/getting-started/pytorch/Thu, 25 Apr 2024 08:00:00 +0200https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/chainguard/chainguard-images/getting-started/pytorch/Chainguard’s PyTorch container image provides a security-hardened foundation for deep learning workloads with significantly fewer vulnerabilities than traditional PyTorch containers. Built with PyTorch and CUDA support for GPU acceleration, this minimal image maintains full deep learning capabilities while dramatically reducing attack surface. This guide demonstrates fine-tuning models, secure inference deployment, and compares the enhanced security posture to official PyTorch images. What is Deep Learning? Deep learning is a subset of machine learning that leverages a flexible computational architecture, the neural network, to address a wide variety of tasks.https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/software-security/learning-labs/ll202507/Thu, 24 Jul 2025 17:00:00 +0000https://deploy-preview-3174--ornate-narwhal-088216.netlify.app/software-security/learning-labs/ll202507/The July 2025 Learning Lab with Patrick Smyth covers AI with Hardened Container Images. In this session, learn how to secure AI workloads by reducing vulnerabilities in container images by over 90%. Patrick demonstrates hands-on techniques for training an animal detection model using PyTorch with hardened container images, creating minimal and secure deployments, and running AI frameworks with zero CVEs. Sections 0:00 Introduction and updates 2:02 Preparation: Docker pull instructions for demo 3:39 Chainguard!