Samprakshi Infinity Solution

ModelOps & ML Engineering

Design, deploy and operate machine learning systems with production-grade serving, CI/CD, monitoring, feature stores and governance.

Showcase 1

Model Serving & Scalable Inference

Stateless and stateful model serving (REST/gRPC/GRPC-Web) with autoscaling, batching and GPU support to meet production SLAs.

ServingInference
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CI/CD for ML & Model Registry

Automated pipelines for training, validation, model versioning and rollout (canary/blue-green) with model registry integration.

CI/CDRegistry
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Monitoring, Observability & Drift Detection

Monitoring for data and concept drift, latency and accuracy regressions, with alerting and automated retraining triggers.

MonitoringDrift
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Data Pipelines & Feature Store

Reliable data ingestion, transformation and a feature store that ensures feature parity between training and serving environments.

DataFeature Store
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Optimization & Cost Management

Model compression, batching and autoscaling strategies to optimize inference cost while maintaining performance and latency targets.

OptimizationCost
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Governance, Security & Compliance

Model lineage, access controls, audit logs and privacy-aware deployment options (on-prem/VPC/edge) to meet regulatory requirements.

GovernanceSecurity
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Frequently asked questions

MLOps is the practice of applying DevOps principles to ML: reproducible pipelines, CI/CD, monitoring and governance so models remain reliable in production.
We validate models with shadow testing, runbook automation, alerting on drift/regressions and automated rollback or incremental rollouts to minimize risk.
Yes — we integrate with major cloud providers, Kubernetes platforms, and on-prem infrastructures using standard APIs, operators and IaC patterns.
Costs depend on inference scale, data volumes and retention. We provide cost estimates and optimizations (batching, caching, model sizing) during discovery.
We use model registries with immutable versioning and support canary/blue-green rollouts plus automated rollback if metrics degrade.