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Cake for Dataset Creation

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

 

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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

  • Speed up iteration: Reuse workflows across training, fine-tuning, and evaluation pipelines.

  • Build on trusted tools: Label, structure, and generate data using open-source frameworks like Hugging Face and Label Studio.

  • Keep it compliant and portable: Ensure your datasets are secure, reproducible, and cloud agnostic. Comply with industry regulations like SOC2 and HIPAA.

  • Support diverse data sources: Ingest and unify structured, semi-structured, and unstructured data from files, APIs, and databases.

  • Enable collaboration at scale: Version datasets, manage permissions, and streamline feedback loops across teams and environments.

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Increase in
MLOps productivity

 

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Faster model deployment
to production

 

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Annual savings per
LLM project

THE CAKE DIFFERENCE

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From messy data to
model-ready datasets

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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
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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

EXAMPLE USE CASES

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How teams use Cake's
dataset creation tooling

 

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Supervised model training

Create and manage labeled datasets for classification, regression, or forecasting tasks.

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LLM fine-tuning

Curate conversational, structured, or synthetic datasets to refine large language models.

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Feature store population

Transform raw data into reusable, versioned, production-ready feature sets for downstream pipelines.

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Balanced dataset generation

Automatically rebalance datasets across key attributes (e.g., class labels, geographies, demographics) to reduce model bias and improve generalization.

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Multi-source feature stitching

Join records across disparate systems using entity resolution or embedding-based matching to build richer training datasets.

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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.

Read More >

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.

Read More >

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"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."

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Scott Stafford
Chief Enterprise Architect at Ping

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"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

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Felix Baldauf-Lenschen
CEO and Founder

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?

What types of data can I prepare with Cake?

Why not just use off-the-shelf datasets?

How does Cake ensure compliance and security in dataset creation?

Learn more about Cake

 

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