Perimattic MLOps Services - ML Pipeline Automation on AWS

Operationalize Machine Learning on AWS with Perimattic

Perimattic helps organizations move machine learning from experimentation to production by building secure, automated MLOps platforms on AWS. With offices in Mumbai, San Francisco, and London and follow-the-sun coverage, our engineering teams design production-ready ML workflows that reduce deployment cycles, improve model accuracy, and lower operational overhead using Amazon SageMaker, Amazon Bedrock, and cloud-native AWS services.

Who We Serve

Our MLOps services are designed for mid-market and enterprise teams - particularly SaaS companies, fintech firms, and healthcare AI teams - that need to ship reliable models faster without building internal platform engineering capacity from scratch. Whether you are deploying your first production model or scaling to hundreds of inference endpoints, we tailor engagements to your maturity level.

How an Engagement Works

We follow a phased delivery model so you always know what comes next:

Phase 1 - Discovery and Assessment (1-2 weeks) Scoping call to understand your ML workloads, data landscape, and business objectives MLOps maturity assessment covering pipelines, monitoring, governance, and team workflows Deliverable: Architecture blueprint and prioritized roadmap

Phase 2 - Design and Implementation (4-12 weeks depending on scope) Build automated ML pipelines using Amazon SageMaker Pipelines or Step Functions Implement CI/CD for model training, validation, and deployment Configure model registry, feature store, and data pipeline automation Deploy infrastructure as code (Terraform/CDK) for reproducibility Deliverables: Deployed pipelines, IaC repositories, runbooks, and operational documentation

Phase 3 - Monitoring, Governance, and Handoff (2-4 weeks) Set up model monitoring, drift detection, and alerting Implement governance controls including access policies, audit logging, and lineage tracking Knowledge transfer sessions with your data science and engineering teams Deliverables: Monitoring dashboards, governance playbook, and team training materials

Phase 4 - Managed MLOps Support (ongoing, optional) Proactive monitoring and incident response for production ML systems Model retraining orchestration and performance optimization Available as business-hours or 24x7 enterprise support

Example Use Case

A SaaS company running a churn prediction model serving thousands of daily predictions needed automated retraining when data drift was detected. Perimattic implemented an end-to-end pipeline on SageMaker - from feature engineering through automated retraining, validation gates, and canary deployment - reducing the manual retraining cycle and enabling the team to iterate on model improvements without DevOps bottlenecks.

Core Services Amazon SageMaker Implementation - Pipelines, endpoints, model registry, and feature store Amazon Bedrock Integration - Foundation model orchestration and RAG workflows ML CI/CD - Automated testing, validation, and deployment for ML artifacts Model Monitoring and Observability - Drift detection, performance tracking, and alerting Infrastructure as Code - Terraform and CDK for reproducible ML environments Kubernetes for ML - Container orchestration for training and inference workloads Model Governance and Security - IAM policies, encryption, audit trails, and lineage

Prerequisites Active AWS account with appropriate service limits Existing data sources or data lake accessible from AWS Designated point of contact on your team (product owner or ML lead) For managed support: production workloads already deployed or ready for deployment

Get Started

Contact us to schedule a free 30-minute MLOps discovery call where we assess your current state and recommend next steps. Reach out via the AWS Marketplace contact mechanism or visit perimattic.com to book a consultation.