ML.NET

End-to-End in C# and F#

Build data loaders, transforms, trainers, and evaluators directly in .NET so pipelines stay explicit and testable. Strong typing surfaces schema issues early, and unit tests validate featurization and scoring like any other code path. Because everything is code, peers review transformations via pull requests, preserving standards and knowledge across teams. Clear step boundaries make debugging straightforward when new data or schema changes arrive unexpectedly.

AutoML and Experimentation

Automate algorithm selection and tuning for classification, regression, and recommendation, then compare runs on metrics you define. Time budgets and early stopping prevent runaway jobs, and reports document choices for audits. Metric dashboards weigh F1, AUC, and latency together, while notes capture tradeoffs so stakeholders understand why a model wins. Reusable experiment recipes accelerate new problem types without reinventing scaffolding each sprint.

Data Access and Featurization

Read from CSV, Parquet, or databases; apply normalization, text processing, and categorical encodings; and extend with custom transforms that reflect domain specifics cleanly. Caching speeds iteration on large sets, and sampling builds quick slices for debugging or demos without moving entire corpora. Shareable featurization pipelines align training and serving to avoid skew, while assertions and checks guard against silent drift in changing production data.

Deployment and Interop with ONNX

Export or import ONNX models, run on CPU or GPU, and host scoring endpoints in ASP.NET APIs or background workers across platforms. Versioning makes rollbacks predictable when behavior changes. Edge and container targets support kiosks or services, queues handle bulk jobs off-peak, and shadow deployments test new versions before cutover. Traffic splitting enables gradual rollout with rollback hooks for safe, measured adoption in production.

MLOps with Familiar Tools

Track artifacts with repositories, package models, and integrate CI/CD so updates ship with the same rigor as app code. Diagnostics surface drift and failed assumptions early. Scheduled evaluations watch for performance decay and alert owners, while lineage links models to data and code commits for traceability. Playbooks describe retraining triggers and approvals so updates follow the governance your organization already uses for production changes.