DAG-based workflow orchestration for batch pipelines.

SQL-first transformations with tests and lineage.

Git-style version control for datasets and models.

Open-source feature store for online + offline parity.

Experiment tracking, model registry, and packaging.

Hosted experiment dashboards and sweeps.

Distributed training across heterogeneous clusters.

Hyperparameter optimization with pruning.

Container runtime for reproducible model environments.

Standardized model packaging with serving APIs.

Stage-gated model promotion (Staging → Production).

Sign and verify container images for supply chain safety.

Kubernetes-native model serving with autoscale-to-zero.

Advanced inference graphs, A/B, and shadow traffic.

High-throughput inference for GPU + CPU backends.

Service mesh for canary, mTLS, and traffic shaping.

End-to-end ML pipelines on Kubernetes.

Container-native workflow engine.

Strongly-typed, reproducible workflow orchestration.

CI/CD for model code, configs, and infrastructure.

Time-series metrics for serving infrastructure.

Dashboards for latency, throughput, and drift.

Open-source data and model drift reports.

Hosted observability for ML data and predictions.