Build the infrastructure that keeps AI in production — ML pipelines, model registries, feature stores, serving infrastructure, and drift monitoring. Kansoft delivers MLOps systems that take models from notebook to production and keep them performing reliably at scale.
The gap between a working model and a reliable production system is where most ML value is lost. These are the bottlenecks we solve.
Data science teams build excellent models but have no repeatable, automated process to deploy them to production environments.
Models deployed without drift monitoring silently degrade as data distributions shift — often discovered only after business impact.
Without a model registry and experiment tracking, teams can't reproduce results, compare models, or roll back safely.
Hand-built serving scripts fail under load, lack versioning, and require manual intervention for every model update.
Training and serving use different feature engineering code, causing training-serving skew that degrades model performance.
Data scientists and engineers work in separate toolchains with no shared infrastructure, creating a costly handoff every deployment.
We treat ML deployment with the same engineering rigour as production software: versioned pipelines, automated testing, monitoring from day one, and clear rollback paths. The output is an ML system your team can operate confidently.
Automated training pipelines with data validation, feature engineering, model training, evaluation gates, and registry push — triggered on schedule or data arrival.
Low-latency inference endpoints with blue/green deployment, canary releases, A/B testing support, and auto-scaling under variable load.
Centralised feature store eliminating training-serving skew — one feature definition used consistently across training, serving, and batch scoring.
Data drift, concept drift, and prediction drift monitoring with automated alerting and retraining triggers when model performance degrades.
Reproducible, versioned training pipelines on Kubeflow, MLflow, or SageMaker Pipelines with data validation and model evaluation gates.
REST and gRPC inference endpoints with BentoML, TorchServe, or Triton — auto-scaling, latency SLAs, and multi-model serving.
Online and offline feature stores (Feast, Tecton, or cloud-native) that eliminate training-serving skew and enable feature reuse.
Centralised model registry with lineage tracking, stage promotion workflows (staging → production), and audit history.
ML-specific monitoring for data drift, prediction drift, data quality, and business KPI correlation — with Grafana dashboards and alerting.
Triggered retraining pipelines that activate on drift detection, new data arrival, or schedule — with human approval gates before production.
We implement MLOps infrastructure in a phased approach — starting with immediate deployment pain, then building out the full lifecycle.
Audit existing model deployment process, data infrastructure, compute environment, and monitoring gaps. Identify the highest-pain MLOps bottleneck.
Set up experiment tracking (MLflow or W&B), model registry, and a first automated training pipeline for your highest-priority model.
Deploy model serving layer with CI/CD integration, blue/green deployment, and basic prediction monitoring.
Implement feature store to eliminate training-serving skew, add data quality checks to ingestion pipelines, and backfill historical features.
Full drift monitoring stack, Grafana dashboards, PagerDuty/Slack alerting, and automated retraining trigger configuration.
Operational runbooks, on-call playbooks, team training on platform operations, and 30-day hypercare support period.
A lending platform's data science team waited 3 weeks per model deployment due to manual handoff to DevOps. We built a Kubeflow-based pipeline with automated evaluation gates and self-service deployment. Time-to-production dropped to 4 hours.
A recommendation engine was underperforming due to inconsistent feature engineering between training and serving code. We implemented Feast, unified feature definitions, and backfilled historical features. Model accuracy improved by 8% without retraining.
A predictive maintenance model was silently degrading after sensor hardware upgrades changed data distributions. We implemented Evidently AI drift monitoring with automated retraining triggers. Three drift events detected and resolved before impacting production decisions.
We combine data engineering, ML engineering, and platform engineering skills in a single team — no handoff between silos.
All infrastructure patterns we implement have been tested under production load. We don't bring untested architectural novelty to client environments.
MLOps systems architected for auditability, model lineage tracking, and data residency requirements. GDPR, HIPAA, SOC 2, and EU AI Act aligned.
We implement on AWS SageMaker, Google Vertex AI, Azure ML, Kubernetes on-prem, or hybrid configurations — whichever matches your cloud strategy.
Complete platform handover with operational runbooks, architecture decision records, and on-call playbooks. Your team owns the infrastructure.
Teams across India, UAE, USA, Europe, and Australia — same-day responses and workday overlap regardless of your timezone.
Not necessarily. Even a single production model benefits from automated retraining triggers, drift monitoring, and a proper versioned deployment pipeline. The question is scale: if you're deploying manually and have no drift monitoring, that one model is a silent risk. We offer a lightweight starter implementation suitable for small ML programmes that can grow with you.
DataOps covers the data engineering pipeline — ingestion, transformation, quality, and storage. MLOps builds on top of data infrastructure and covers the model lifecycle specifically: training pipelines, experiment tracking, model registry, serving, and monitoring. Both are needed; we work on both, and can connect them if you need an end-to-end data-to-model platform.
We're platform-neutral and recommend based on your existing cloud footprint and team skills. SageMaker (AWS), Vertex AI (GCP), and Azure ML are all mature and capable. For cost-sensitive workloads or data sovereignty requirements, we implement on Kubernetes with open-source tools (Kubeflow, MLflow, Feast). The right choice depends on your constraints.
Every model deployed through our pipelines is versioned in the model registry with full lineage (training data version, code version, hyperparameters, evaluation metrics). Rollback is a single command that re-routes traffic to the previous model version. We also implement canary deployments so new models serve a small traffic percentage before full promotion.
We support all major ML frameworks: scikit-learn, XGBoost, PyTorch, TensorFlow, JAX, and Hugging Face transformers. For LLM serving specifically, we implement vLLM, TGI, or managed endpoints depending on latency and cost requirements. Batch scoring, real-time inference, and streaming are all supported.
