AI/ML Solutions
End-to-end ML systems - data pipelines, model training with PyTorch or TensorFlow, experiment tracking, and production deployment with FastAPI and Docker. MLOps-focused engineering, not demo projects.
- model training
- MLOps
- FastAPI
What is included
- Data pipelines and model training with PyTorch or TensorFlow
- Experiment tracking and evaluation workflows
- Production deployment with FastAPI and Docker
- MLOps-focused engineering beyond demo notebooks
Typical projects
Turn a notebook prototype into a deployed FastAPI service
Set up experiment tracking and repeatable model evaluation
Build data pipelines that feed training and retraining
Containerise models with Docker for consistent deployment
How I Work
Inquiry
Tell me what you are building and the outcome you want.
Scope Call
A short call to pin down goals, constraints and timeline.
Proposal
A written scope, timeline and price before any work starts.
Build
Iterative delivery with regular progress updates.
Delivery
Deployed, documented and handed over cleanly.
Questions
- What does MLOps-focused engineering mean here?
- The model is only one part of the delivery. I also build the data pipeline, experiment tracking, evaluation workflow and deployment path, so the system keeps working after the first demo.
- Which ML frameworks do you work with?
- PyTorch and TensorFlow for training, with FastAPI and Docker for serving and deployment.
- How long does an ML project take?
- ML prototypes usually take 2-4 weeks. Full production systems take 1-3 months, depending on data readiness and integration scope.
- Can you take over an existing model or notebook?
- Yes. A common starting point is an existing notebook or prototype that needs a pipeline, evaluation and a deployable service around it.
Other services
Not sure which service fits or have more questions? Let's talk - I read everything