Build and ship production ML pipelines faster: a pipeline library with an optional self-hosted visual layer for modular, reproducible workflows, local testing, and experiment tracking.

Small on-prem machine learning ecosystem for small-data environments, where fast iteration, maintainability, and reliable deployment matter more than large-scale infrastructure.

Shared development toolbox for engineers. Provides reusable data + modeling pipelines and a unified packaging/deployment client for ml-deployment-ecosystem. Not for storage of models/data or high-frequency production extraction.

An AI-powered cloud threat detection system with a full MLOps lifecycle multi-source log ingestion, unsupervised anomaly detection, MITRE ATT&CK mapping, CVE enrichment, and a Claude-powered SOC analyst, all wired into a Kubeflow pipeline that trains, gates, and deploys to KServe automatically.