Custom ML Model Development & MLOps

When an off-the-shelf model or API genuinely isn't the right fit — proprietary data, a highly specialized prediction task, or strict data-residency requirements — we build custom ML models from the ground up using PyTorch, TensorFlow, or scikit-learn, with feature engineering and evaluation methodology matched to the specific problem rather than a generic training pipeline.

Custom models are only valuable if they keep working after launch, so MLOps is scoped as part of the engagement, not an afterthought: experiment tracking and model versioning with MLflow, training and deployment pipelines on platforms like Kubeflow, AWS SageMaker, or Vertex AI, and production monitoring for drift and performance degradation, with a defined retraining cadence rather than a model quietly going stale.

• Custom model training with PyTorch, TensorFlow, or scikit-learn
• Experiment tracking and model versioning with MLflow
• Training/deployment pipelines on Kubeflow, SageMaker, or Vertex AI
• Production monitoring for model drift and performance degradation
• Defined retraining cadence rather than one-time deployment