ML Model Training & Deployment
Train, optimize, and integrate ML/DL models with backend APIs and deployment performance in mind.
What it covers
Develop and operationalize machine learning models for prediction, classification, and automation. Includes supervised/unsupervised workflows, deep learning with PyTorch/TensorFlow/Keras, API integration, and deployment-focused optimization drawn from part-time AI engineering and freelance ML delivery.
Business value
Move models from experiment to usable API-backed systems with clearer performance and integration paths.
Features
- Custom model training and evaluation
- Transfer learning and fine-tuning
- Backend API integration
- Performance-minded deployment
- Versioning and iteration support
Process
- Problem & metrics — Lock the prediction target, data constraints, and evaluation metrics.
- Train & iterate — Build baselines, improve models, and validate against real samples.
- Integrate & deploy — Serve the model through APIs and improve runtime/deployment behavior.
Technologies
- Python
- PyTorch
- TensorFlow
- Keras
- scikit-learn
- FastAPI
- Docker
- GitHub Actions
- CI/CD
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