I will prepare clean labeled datasets for your ai or machine learning project
Aero Engineering Student, 4 years in ML, DRL based Control for UAV
About this Gig
Your model is only as good as the data you feed it. I learned this the hard way training diffusion models on custom datasets: the fastest way to fix bad output was almost always fixing the data.
I prepare and quality-check training data for machine learning and computer vision projects:
- Cleaning and organizing raw image sets (dedupe, remove corrupt or off-topic files)
- Labeling and annotation against your guidelines
- Train, validation, and test splits with manifest files in CSV or JSONL
- A written quality report flagging inconsistencies, ambiguities, and edge cases before they become expensive training failures
Typical clients: Training data for AI startups, researchers, and teams building computer vision models who want a second pair of eyes on dataset quality before training.
Send me a sample of your data and your goal. I'll review it and tell you exactly what needs fixing before you spend money on compute.
Technique:
Manual
Tagging type:
Text
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Image
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Audio
My Portfolio
FAQ
What formats do you work with?
Images in any common format, CSV, JSONL, plain text. Deliverables come in whatever your pipeline imports.
Which annotation tools?
I can work inside your tool (CVAT, Labelbox, or similar) or deliver structured label files you import yourself.
Can you follow custom annotation guidelines?
Yes. Send the guidelines plus a few examples, and I'll do a small paid sample batch first so we agree on quality before scaling up.
Is my data confidential?
Yes. I work under NDA on request and delete project data after delivery.
How do you check quality?
Every batch gets a second-pass review, and you receive a written report of what was found and fixed.

