Aatizaz Khan
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Service

Model Training & Fine-Tuning

I train and fine-tune machine learning models on your data, evaluate them against clear metrics, and deploy them to the cloud. Work includes computer vision models such as medical image segmentation, where one of my models reached 89% accuracy.

What you get

  • Data preparation and training pipelines
  • Fine-tuned models built on pretrained foundations
  • Evaluation reports against agreed metrics
  • Deployment behind an API on AWS

Tech stack

  • Python
  • TensorFlow
  • Hugging Face
  • OpenCV
  • FastAPI
  • AWS
  • Docker

How it works

  1. 01

    Scope

    You share the idea and requirements; I reply within two days with questions, then a fixed-price quote that itemises each deliverable.

  2. 02

    Build

    Work happens in stages with regular progress updates. Changes mid-build are assessed and planned in, not refused.

  3. 03

    Review

    Two rounds of revisions are included. Anything beyond that, or a change in scope, is quoted separately.

  4. 04

    Launch & support

    Release, handover, and two weeks of support included. Ongoing care is available as a monthly retainer.

Timeline & pricing

Scoped per project; you get a fixed timeline with the quote. Every project has a fixed price, quoted after I review your requirements, with each deliverable itemised.

Questions

It depends on the task. Fine-tuning a pretrained model usually needs far less data than training from scratch, because the model already understands general patterns. I review your dataset first and tell you plainly whether it is enough, or what to collect.

Trained models are deployed behind a FastAPI service on AWS (Lambda, EC2 or S3 for storage), packaged with Docker for consistent releases. Your application then calls the model through a simple API.

Other services

Available for work

Got an idea worth building?

Start a project

Start a project