Cake for Dataset Creation
Label, structure, and generate high-quality datasets for supervised and GenAI using open-source tools on a cloud-agnostic foundation.






Overview
Models are only as good as the data they’re trained on, and most teams spend a disproportionate amount of time collecting, labeling, and transforming datasets. Whether you’re building supervised models or fine-tuning a foundation model, dataset creation is where quality starts.
Cake integrates open-source tools like Label Studio, Hugging Face, and Feast into a unified platform. Annotate raw data, generate synthetic samples, track feature versions, and manage labeling workflows with reproducibility and orchestration built in.
Because Cake is cloud agnostic, you’re never locked into a single storage backend or labeling workflow. Your data workflows remain portable, reproducible, and easy to integrate with downstream training and evaluation pipelines.
Key benefits
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Speed up iteration: Reuse workflows across training, fine-tuning, and evaluation pipelines.
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Build on trusted tools: Label, structure, and generate data using open-source frameworks like Hugging Face and Label Studio.
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Keep it compliant and portable: Ensure your datasets are secure, reproducible, and cloud agnostic. Comply with industry regulations like SOC2 and HIPAA.
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Support diverse data sources: Ingest and unify structured, semi-structured, and unstructured data from files, APIs, and databases.
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Enable collaboration at scale: Version datasets, manage permissions, and streamline feedback loops across teams and environments.
THE CAKE DIFFERENCE
From messy data to
model-ready datasets
Manual / ad hoc data prep
Slow, messy, and hard to reproduce: Most teams rely on one-off scripts, spreadsheets, and duct-taped tools to build datasets.
- Manual cleaning, deduplication, and formatting
- Inconsistent labeling across sources and annotators
- No lineage or reproducibility across versions
- Hard to scale beyond one use case or team
Result:
Time-consuming, error-prone, and difficult to maintain
Dataset creation with Cake
Structured, automated, and scalable: Use Cake to create high-quality, versioned datasets across domains and modalities.
- Integrates OSS tools for labeling, QA, versioning, and enrichment
- Supports text, images, PDFs, chat logs, tabular, and more
- Built-in workflows for deduplication, anonymization, and filtering
- Full lineage, reproducibility, and multi-team collaboration
Result:
Higher quality datasets, less manual work, and faster model development
EXAMPLE USE CASES
How teams use Cake's
dataset creation tooling
Supervised model training
Create and manage labeled datasets for classification, regression, or forecasting tasks.
LLM fine-tuning
Curate conversational, structured, or synthetic datasets to refine large language models.
Feature store population
Transform raw data into reusable, versioned, production-ready feature sets for downstream pipelines.
Balanced dataset generation
Automatically rebalance datasets across key attributes (e.g., class labels, geographies, demographics) to reduce model bias and improve generalization.
Multi-source feature stitching
Join records across disparate systems using entity resolution or embedding-based matching to build richer training datasets.
Synthetic data generation
Use generative models or rule-based approaches to create synthetic datasets when real data is scarce, sensitive, or imbalanced.
IN DEPTH
Unlock smarter models with fine-tuned datasets
Turn raw data into high-performing, domain-specific models. Cake makes it easy to fine-tune with reusable pipelines, trusted open-source tools, and built-in compliance.
SUCCESS STORY
How Ping scaled AI faster (and cheaper) with Cake
Ping boosted productivity, cut costs, and kept data secure by building its AI stack on Cake. See how better dataset practices powered real business results.
"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

Scott Stafford
Chief Enterprise Architect at Ping
"With Cake we are conservatively saving at least half a million dollars purely on headcount."
CEO
InsureTech Company
COMPONENTS
Tools that power dataset creation
on Cake

DVC
Data Versioning
DVC (Data Version Control) is an open-source version control system for machine learning projects. Cake integrates DVC to enable reproducible ML pipelines, data lineage, and collaborative model development.

Label Studio
Data Labeling & Annotation
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.

Promptfoo
LLM Observability
LLM Optimization
Promptfoo is an open-source testing and evaluation framework for prompts and LLM apps, helping teams benchmark, compare, and improve outputs.
Frequently asked questions
What is dataset creation in AI?
Dataset creation is the process of collecting, labeling, structuring, and preparing data so it can be used effectively for training, fine-tuning, and evaluating AI models. High-quality datasets are essential for accurate and reliable outcomes.
How does Cake support dataset creation?
Cake streamlines dataset creation by integrating open-source labeling and preprocessing tools into a secure, cloud-agnostic stack. Teams can automate workflows, reuse pipelines across projects, and maintain compliance without managing heavy infrastructure.
What types of data can I prepare with Cake?
Cake supports structured, semi-structured, and unstructured data, including text, images, audio, video, and tabular formats. This flexibility allows enterprises to create training datasets for a wide range of AI applications.
Why not just use off-the-shelf datasets?
Off-the-shelf datasets are useful for experimentation but rarely align with the specific data, compliance, or domain context your business requires. Creating your own datasets ensures relevance, accuracy, and ownership.
How does Cake ensure compliance and security in dataset creation?
All data handling takes place within your secure environment. Cake provides full auditability, lineage tracking, and adherence to standards like SOC 2 and HIPAA, so sensitive information remains protected throughout the process.
Learn more about Cake

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