Classic ML AI frameworks

Yes — but not in exactly the same way as Delta Lake.

Classic AI/ML doesn’t have one single universal layer identical to “Delta Lake”, but it does have a set of frameworks that play similar roles for models, data, and pipelines. The closest equivalents depend on what part of Delta Lake you are comparing to.


Below is a clear mapping.





First: What Delta Lake is in Data Engineering



Delta Lake provides:



  • A reliable storage layer
  • Versioning of data
  • Schema enforcement
  • ACID transactions
  • Reproducibility



So your question is basically:

👉 “Is there a similar standardized layer for AI models?”





✅ The AI/ML equivalents (by function)




1) If you mean: “A Delta Lake for ML data” → Feature Stores



This is the closest analogy for AI to Delta Lake.


Examples:



  • Feast
  • Google Vertex AI Feature Store
  • Databricks Feature Store
  • AWS SageMaker Feature Store



These provide:



  • Versioned features
  • Consistent training vs. inference data
  • Data governance
  • Reproducibility



👉 Think of this as “Delta Lake for ML features.”





2) If you mean: “A Delta Lake for models” → Model Registry



Instead of storing tables, you store models.


Examples:



  • MLflow Model Registry
  • Vertex AI Model Registry
  • SageMaker Model Registry



These provide:



  • Model versioning
  • Staging → Production lifecycle
  • Audit trail
  • Rollback capability



👉 This is the closest “governance layer for AI models.”





3) If you mean: “A framework like Spark + Delta Lake” → End-to-End ML Platforms



These combine training, tracking, and deployment:



  • MLflow
  • Kubeflow
  • TensorFlow Extended (TFX)
  • Vertex AI Pipelines
  • Ray + Ray Serve



These act like:



  • Spark = execution engine
  • Delta Lake = reliability layer



But in ML form.





4) If you mean: “Versioning like Delta Lake” → Data & Experiment Tracking



Tools that track versions of data, code, and experiments:



  • DVC (Data Version Control)
  • MLflow Tracking
  • Weights & Biases (W&B)



These ensure:



  • You can reproduce past model results
  • You know which data trained which model


Delta Lake Role

AI/ML Equivalent

Reliable data layer

Feature Store (Feast, Vertex AI FS)

Table versioning

DVC / MLflow tracking

Governance

Model Registry (MLflow / Vertex AI)

Processing engine (Spark)

Kubeflow / TFX / Ray




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