Results for “system-versioning”

Versioning exists because you cannot deploy your clients.

ML pipeline design — data versioning, experiment tracking, deployment patterns, drift monitoring. Use when building an ML pipeline from data to deployment, setting up MLOps tooling (DVC, MLflow, model registry), choosing deployment patterns (shadow, canary, A/B), or designing monitoring for drift and degradation.

Data Version Control for ML projects. Track large datasets and models alongside Git, build reproducible ML pipelines, and run experiments with metric comparison. Works with any storage backend including S3, GCS, Azure, and local filesystems.

Automates semantic versioning and release workflow for Claude Code plugins, including version increments across multiple manifest files, build verification, git tagging, GitHub releases, and changelog generation.

Identify, test, deploy, and verify software updates across an organization's IT infrastructure using a structured patch management workflow with phased rollouts and automated assessment.

Deploy a trained model to serving with versioning, shadow or canary rollout, and a tested rollback path.

`analysis-agent`/`task-agent`/`review-agent`: use for release identity, compatibility, exposure, stop, rollback, or recovery; skip pipeline-only and no-release work.

Provides Git-like version control for ArcGIS web maps, exposing branch, commit, diff, push/pull, and map discovery as callable tools.

Build, deploy, and manage Redpanda Data Transforms, WebAssembly functions that run inside the broker to transform records from an input topic to one or more output topics. Use when writing or deploying a transform in Go (TinyGo), Rust, JavaScript, or TypeScript; using `rpk transform.