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THE CAKE DIFFERENCE
From duct-taped pipelines to
secure, scalable MLOps
DIY MLOps stacks
Glue code and guesswork at every stage: Teams spend more time stitching tools together than shipping value.
- Every pipeline requires custom orchestration and infra setup
- Observability, versioning, and access control are bolted on
- Compliance, security, and auditability are manual and inconsistent
- Hard to scale across teams, clouds, or deployment targets
MLOps with Cake
Composable MLOps that works out of the box: Cake gives you a pre-integrated stack with observability, governance, and scale.
- Pre-wired tools for training, tuning, deployment, and monitoring
- Built-in lineage, model registry, and evals across workflows
- SOC 2, HIPAA, and access controls ready from day one
- Deploy across clouds or on-prem with full portability and control
EXAMPLE USE CASES
How teams are using Cake’s
MLOps components
Experiment tracking
Log datasets, hyperparameters, and results from notebooks and workflows for comparison and reuse.
Model serving
Deploy models behind inference endpoints with autoscaling, versioning, and rollout strategies.
Performance monitoring
Track latency, error rates, and drift with integrated metrics and alerts.
Cross-environment model promotion
Move models seamlessly from dev to staging to production with consistent configs, versioning, and rollback support.
Automated retraining pipelines
Trigger model retraining based on drift detection, data freshness, or business rules without manual intervention.
Unified model and data lineage
Track which datasets, code versions, and parameters were used in every model run to support reproducibility and audits.
Scott Stafford
Chief Enterprise Architect at Ping
CEO
InsureTech Company
Felix Baldauf-Lenschen
CEO and Founder
COMPONENTS
Tools that power Cake's MLOps stack
Ray Tune
Ray Tune is a Python library for distributed hyperparameter optimization, built on Ray’s scalable compute framework. With Cake, you can run Ray Tune experiments across any cloud or hybrid environment while automating orchestration, tracking results, and optimizing resource usage with minimal setup.
Unity Catalog
Unity Catalog is Databricks’ unified governance solution for managing data access, lineage, and discovery across your Lakehouse. With Cake, Unity Catalog becomes part of your compliance-ready AI stack, ensuring secure, structured data usage across teams and environments.
Apache Iceberg
Apache Iceberg is an open table format for managing petabyte-scale analytic datasets. Cake integrates Iceberg into AI workflows, making it easy to handle versioned, partitioned data across storage layers.
MLflow
Track ML experiments and manage your model registry at scale with Cake’s automated MLflow setup and integration.
Kubeflow
Kubeflow is an open-source machine learning platform built on Kubernetes. Cake operationalizes Kubeflow deployments, automating model training, tuning, and serving while adding governance and observability.
Feast
Feast is an open-source feature store for managing and serving machine learning features. Cake integrates Feast into AI pipelines, automating feature storage, retrieval, and governance.
Label Studio
Label Studio is an open-source data labeling tool for supervised machine learning projects. Cake connects Label Studio to AI pipelines for scalable annotation, human feedback, and active learning governance.
Evidently
Evidently is an open-source tool for monitoring machine learning models in production. Cake operationalizes Evidently to automate drift detection, performance monitoring, and reporting within AI workflows.
XGBoost
XGBoost is a scalable and efficient gradient boosting library widely used for structured data and tabular ML tasks.
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