Cut ML pipeline setup complexity down to hours
Deepline starts by automating setup with Airflow and MLflow. Monitoring, retraining, and cloud portability are planned next.
"In ML, we often spend more time trying to operationalize and maintain models than actually building them. The hardest part is not the algorithms, but making them work in a production setting."
— Andrew Ng, Deeplearning.ai
Why (The Pain)
Pipelines stall on setup and upkeep. Complex stack sprawl with MLflow + Airflow + custom scripts means setup drag eats weeks before value appears. Ops debt builds before teams even reach monitoring or retraining.
How (Our Approach)
We solve setup first, then expand. Our current focus is Scaffold - generating pipelines with minimal configuration to get you from weeks to hours. Next comes Observe with unified drift and performance monitoring, followed by Adapt for safe retraining workflows, and finally Port with cloud-agnostic runners.
What (The Product)
Integration wizard for MLflow + Airflow (current). Auto-generated eval dashboards, PII/data quality guardrails, and retraining schedulers with rollback (planned roadmap).
What We're Building
🔧 Integration Wizard
Available NowAutomated MLflow and Airflow setup with minimal configuration. Generate production-ready pipelines from a single YAML file and two commands.
📊 Auto-Generated Eval Dashboards
PlannedComprehensive evaluation dashboards that automatically surface model performance metrics, data drift indicators, and pipeline health status.
🛡️ Guardrails: PII Checks & Data Quality Gates
PlannedBuilt-in protection against data leaks and quality issues. Automated PII detection and configurable data quality thresholds before model training.
🔄 Retraining Schedulers with Rollback
PlannedSafe, automated retraining workflows with intelligent rollback capabilities. Deploy with confidence knowing you can always revert to previous model versions.
Get started by simplifying your ML pipeline setup
Join the beta and be among the first to experience ML pipeline setup that takes hours, not weeks. Public beta launching Q4 2025.
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