Machine Learning
Models trained, evaluated and deployed, with the retraining path planned before the first release.
What this covers
- Training and evaluation pipelines
- Feature stores and versioning
- Inference APIs and serving
- Drift monitoring and retraining
What we build on
Model choice is a dependency, not an identity. We keep the provider swappable and evaluate before switching.
- Claude
- GPT
- Gemini
- Llama
- Whisper
- Embeddings
- Vector search
- RAG
- Function calling
- Evals
Where this is heading
Most ML projects fail after launch, not before it, when live data drifts away from the training set. We instrument for that on day one so the model degrades visibly instead of quietly.