10×
faster model deployment cycle
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Accreditations
Data Services · ML Engineering
The engineering side of AI — feature stores, training pipelines, model registries, deployment automation, monitoring, and retraining. From classical ML to deep learning to LLM operations.
What you get
Models in production. Not in notebooks.
What's in scope
01·Data Services · ML Engineering
Feature stores (Feast, Tecton, Databricks) with online + offline consistency.
02·Data Services · ML Engineering
Repeatable, version-controlled training across SageMaker, Vertex AI, Databricks, Azure ML.
03·Data Services · ML Engineering
Real-time inference, batch scoring, shadow mode, canary rollouts.
04·Data Services · ML Engineering
Monitor data drift, prediction drift, model performance — auto-retrain on threshold breach.
Measured outcomes
10×
faster model deployment cycle
95%
of models in production stay healthy without retraining intervention
50%
lower inference cost via optimisation
Why Xpertnest delivers this
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