Solutions
Route & prepare data for AI/ML — without rewriting pipelines
AI/ML teams need consistent, policy-compliant datasets. LyftData turns exports, documents, and event streams into model-ready data: filter noise, protect sensitive fields, then route the right records to the right destinations.
Problem
Training and evaluation pipelines break when sources change shape. Sensitive fields leak into datasets. Teams fork scripts per environment, then lose track of what ran, where, and why. The result is slow iteration and low trust in the data feeding models.
LyftData Solution
- Extract and normalize: turn PDFs, DOCX, spreadsheets, and JSON into consistent records.
- Filter and protect: remove noise, dedupe, and mask or redact sensitive fields before data leaves its boundary.
- Prepare for models: chunk long text and optionally generate model features or labels as part of the workflow.
- Route to your stack: deliver training sets to warehouses or object storage, and POST to APIs for indexing and serving.
- Stay reproducible: version workflows, validate changes with Run & Trace, and keep lineage for audits and backfills.
One workflow can feed a clean training dataset, a search/indexing pipeline, and a low-cost archive — without duplicating ingestion.
What you get
Faster iteration
Update one workflow when schemas change instead of rewriting code across pipelines.
Cleaner datasets
Standardize fields, remove noise, and keep consistent joins and partitions for analytics and training.
Safer data movement
Enforce masking and redaction upstream, and keep provable lineage for every run.
Next steps
Start with one source and one destination. Add protection and normalization first, then extend the same workflow to feed model training, evaluation, and indexing paths.