Organizations Using the applied-ml Tag for Production Machine Learning, MLOps, and Model Deployment
Explore organizations tagged with applied-ml to discover teams and companies that build production-ready machine learning systems, from feature engineering and supervised learning to transfer learning, model interpretability, and real-time inference. This filtered list of organizations (nav: organizations, pillar: tags) highlights applied machine learning use cases, architecture patterns, and operational practices—covering model deployment, monitoring, CI/CD for models, data pipelines, and scalable inference. Use the filtering UI to narrow results by industry, tech stack (TensorFlow, PyTorch, scikit-learn), deployment target, or maturity level; view case studies, implementation notes, and open-source contributions to compare performance trade-offs and tooling. Actionable next steps: review architecture examples, save or export filtered lists for outreach, and click into organization profiles to evaluate partnerships, hiring, or collaboration opportunities.
