ML systems engineered for production.

Most ML work in production looks like a notebook a data scientist scp'd onto a server. No reproducibility, no monitoring, no rollback, no understanding of when the model is wrong. We bring software engineering discipline to ML: typed code, tested pipelines, versioned data, monitored predictions, reproducible training.

• 01Training pipelines: reproducible, versioned, scheduled
• 02Feature stores for online and offline parity
• 03Model serving: real-time, batch, streaming
• 04Model registry, versioning and rollback
• 05Drift detection: data drift, concept drift, performance drift
• 06Shadow deployment and A/B infrastructure for models
• 07Cost-aware model selection (smaller models, distillation)
• 08MLOps platform: from a managed stack to self-hosted
• 09Recommendation systems, ranking, classification, forecasting
• 10Integration of classical ML alongside LLMs where each wins

• Production ML system with training and serving pipelines
• Monitoring dashboards for drift and performance
• Reproducibility — re-train on a previous data version
• Documentation for data scientists and engineers alike

• → Data science teams whose models never make it to production
• → Companies running ML in production without monitoring or rollback
• → Recommendation, ranking, fraud and forecasting use cases at scale
• → Teams adding classical ML capabilities alongside LLM features