AWS Select Tier Consulting PartnerMLOps
Operationalize ML at scale with production-grade infrastructure
Automated pipelines, model monitoring, and continuous training — because ML in production is an ongoing process, not a one-time deployment.
Challenges We Solve
Getting models to production is just the beginning. Keeping them running is the hard part.
Manual Deployments
Data scientists manually deploy models, leading to inconsistencies, errors, and deployment bottlenecks.
Model Drift
Models degrade over time as data changes, but you have no visibility into when or why.
No Reproducibility
Can't reproduce model training runs or track which version is in production.
Scaling Problems
Managing one model is hard enough — managing dozens across teams is chaos.
What We Build
Enterprise-grade MLOps infrastructure on AWS.
Automated ML Pipelines
End-to-end pipelines that automate data prep, training, validation, and deployment. Push-button model updates.
Model Registry & Versioning
Central repository for all models with version control, approval workflows, and lineage tracking.
Model Monitoring & Observability
Real-time monitoring for data drift, prediction quality, and infrastructure health. Automated alerts and retraining triggers.
Feature Store & Management
Centralized feature repository ensuring consistency between training and inference. Feature versioning and discovery.
Why Choose PATHSDATA
Continuous Training
Models automatically retrain when performance degrades or new data arrives.
Governance & Compliance
Full audit trail, model lineage, and approval workflows for regulated industries.
10x Faster Deployments
What used to take weeks now takes hours with automated pipelines.
Full Reproducibility
Every training run is versioned and reproducible — no more "it worked on my laptop".
Industry Use Cases
Financial Services
Automated credit model retraining with regulatory approval workflows and full audit trails.
E-commerce
Recommendation model pipeline that retrains weekly on new purchase data with A/B deployment.
Healthcare
Diagnostic model monitoring with drift detection and automatic rollback on quality degradation.
Manufacturing
Predictive maintenance pipeline that retrains on new sensor data with champion/challenger testing.
Technology Stack
Platform
- SageMaker
- Kubeflow
- MLflow
- Vertex AI
Pipelines
- SageMaker Pipelines
- Step Functions
- Airflow
Monitoring
- Model Monitor
- CloudWatch
- Evidently
Infrastructure
- EKS
- Lambda
- ECR
- Terraform
Our Process
MLOps Assessment
Evaluate current ML practices, identify gaps, and define target maturity level.
Platform Design
Design MLOps architecture — pipelines, registry, monitoring, feature store based on your needs.
Implementation
Build and configure MLOps infrastructure. Migrate existing models to new platform.
Training & Enablement
Train data science and ML engineering teams. Document processes and runbooks.
Ready to Operationalize Your ML?
Let's build MLOps infrastructure that turns your ML experiments into production assets.