You will get an AI-ready training dataset processed from your messy CSV or Excel files.
Project details
Most AI fine-tuning projects fail because the training data is an unstructured mess. If you feed an LLM duplicate rows, syntax-breaking HTML noise, or poorly balanced conversations, you waste your compute budget on a model that hallucinates.
What sets this project apart is my strict focus on data engineering for AI. I don't just "convert" files; I sanitize them.
I build custom Python pipelines that scrub out text artifacts, mask sensitive PII (emails/phone numbers) for privacy compliance, and restructure your raw CSV, Excel, or SQL data into mathematically optimized, production-ready JSONL datasets. Whether you are aiming for brand-voice cloning, specialized classification, or domain-specific RAG systems, I ensure your data is perfectly validated for OpenAI, Anthropic, or Hugging Face open-source models before you ever click "train."
What sets this project apart is my strict focus on data engineering for AI. I don't just "convert" files; I sanitize them.
I build custom Python pipelines that scrub out text artifacts, mask sensitive PII (emails/phone numbers) for privacy compliance, and restructure your raw CSV, Excel, or SQL data into mathematically optimized, production-ready JSONL datasets. Whether you are aiming for brand-voice cloning, specialized classification, or domain-specific RAG systems, I ensure your data is perfectly validated for OpenAI, Anthropic, or Hugging Face open-source models before you ever click "train."
AI Development Type
Deep Learning, Knowledge Representation, Model TuningAI Tools
Google AutoML, MLflow, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$30
|
Standard
$50
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | - | |
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | - | - | |
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Extra 2,000 Rows Processed
(+ 2 Days)
+$50About Moin Uddin
AI Data Engineer | LLM Fine-Tuning & Dataset Specialist
Sylhet, Bangladesh - 11:49 am local time
I build custom Python data pipelines that transform raw, unstructured corporate data into validated, high-performance training datasets for OpenAI, Anthropic, and open-source models.
How I protect your training compute budget:
• Structure: Programmatically mapping raw CSV, SQL, and PDF data into validated JSONL Chat-Completion schemas.
• Sanitization: Removing structural noise (HTML/Markdown artifacts), deduplicating records, and string-balancing to prevent model overfitting.
• Privacy: Implementing custom Regex pipelines for PII masking (names, emails, phones) to meet strict compliance standards before API upload.
• SFT Targeting: Tailoring dataset token distributions for specific objectives, whether brand-voice alignment, custom classification, or domain-specific RAG.
Technical Stack:
• Core: Python (Pandas, NumPy, Re/Regex)
• Data Formats: JSON, JSONL, Parquet, CSV and many more
• Ecosystem: Hugging Face (Datasets, Transformers), OpenAI & Anthropic APIs
Let's make sure your training run succeeds on the first try. Send over a sample of your raw data, and I’ll diagnose what it needs to get AI-ready.
Steps for completing your project
After purchasing the project, send requirements so Moin Uddin can start the project.
Delivery time starts when Moin Uddin receives requirements from you.
Moin Uddin works on your project following the steps below.
Revisions may occur after the delivery date.
Data Ingestion & Audit
I review your uploaded raw files and system prompt instructions. I check for file corruption, gauge the baseline quality of the data, and confirm your fine-tuning objective.
Data Cleansing & Deduplication
I run custom Python scripts to remove structural noise (HTML/Markdown), drop empty values, and eliminate duplicate records that could cause your AI model to overfit.