HelixML launches Helix AutoOps and raises $18M Series A to simplify MLOps

AI · 4 min read

Helix AutoOps automates model retraining triggers, data drift detection, and blue/green deployment patterns with integrated canary testing. The platform integrates with common data lakes and feature stores while offering policy-driven governance.

HelixML secured $18 million in Series A funding led by Vector Peak with contributions from enterprise-focused investors. The round will enable development of native integrations for on-premises Kubernetes clusters and specialized hardware accelerators.

AutoOps' monitoring suite provides causal analysis tools to pinpoint data pipeline changes and drift sources; it also includes automated rollback and model explainability snapshots for compliance use cases. Engineers can use declarative YAML pipelines or a visual flow builder.

Early adopters, particularly in finance and retail, say Helix AutoOps reduced time-to-detect data issues and simplified cross-team handoffs. HelixML plans a partner program for consultancies to speed enterprise adoption.