Dataiku

Use reusable project templates with standard lifecycle: data quality checks, evaluation, bias tests, approval workflows

Leverage pushdown architecture (SQL, Spark, Kubernetes) so heavy processing runs where data already lives

Define explicit promotion workflows with Dataiku Govern: who can approve, what evidence needed, when to review

Set up built-in monitoring for data drift, model performance, and service health with alerts to right owners

Add data quality rules directly into Flows so broken inputs block downstream steps automatically

Use scenarios as orchestration backbone for data refresh, training pipelines, batch scoring, and reporting

Treat GenAI same as ML: register, version, test with guardrails (toxicity, PII), monitor, and govern