Most AI teams spend more time managing models than improving them. Perimattic designs and implements production-grade MLOps platforms that automate the full ML lifecycle — from data pipelines through model training, deployment, monitoring, and automated retraining.
MLOps Tools and Platforms We Build On — MLflow, Kubeflow, Apache Airflow, DVC, AWS SageMaker, Seldon Core
MLOps (Machine Learning Operations) is a set of practices that combines ML engineering, DevOps, and data engineering to reliably deploy and maintain machine learning systems in production. It automates the full ML lifecycle — from data preparation and model training to deployment, monitoring, and retraining — replacing the fragile, manual handoffs that cause most production AI failures.
The practical implication for business is significant. A fraud detection model deployed without MLOps silently degrades as transaction patterns evolve — and nobody knows until the false-negative rate climbs. With MLOps, drift is detected automatically, a retraining job fires, and the updated model is validated and promoted to production without human intervention. That is the difference between AI that depreciates and AI that improves over time.
Perimattic builds MLOps platforms that cover the complete lifecycle — automated pipelines, centralised model registries, infrastructure-as-code for training and serving environments, drift monitoring, and governance tooling — designed from day one for the scale and compliance requirements of production enterprise systems.
Deployment
Manual notebook runs and ad-hoc scripts
Deployment
Automated CI/CD pipelines with validation gates
Model versioning
File system or ad-hoc naming conventions
Model versioning
Centralised registry with full lineage and metadata
Drift detection
Noticed after complaints or visible failure
Drift detection
Real-time statistical monitoring with automated alerts
Retraining
Manual, infrequent, and error-prone
Retraining
Triggered automatically on drift, schedule, or data events
Compliance
Difficult to reconstruct decisions or audit models
Compliance
Full lineage, audit trail, and approval workflows built in
The distinction matters most for teams managing multiple models across regulated environments — financial services, healthcare, legal, and manufacturing — where model failures carry real business and compliance consequences.
Seven specialist service lines, each designed to solve a specific layer of the MLOps challenge.
End-to-end automated pipelines from data ingestion through training, validation, and deployment using Kubeflow, Airflow, or custom orchestrators tailored to your infrastructure.
Centralised model registry with version control, metadata tracking, lineage capture, and approval workflows for every production deployment.
Infrastructure-as-code for ML workloads — GPU clusters, training environments, and serving infrastructure on AWS SageMaker, Azure ML, or GCP Vertex AI.
Automated retraining triggers based on data drift, performance degradation, or scheduled intervals — ensuring your models stay accurate as the world changes around them.
Audit trails, model lineage, role-based access controls, and approval gates that satisfy SOC 2, HIPAA, and industry-specific regulatory requirements.
Production model serving with auto-scaling, canary deployments, and A/B testing frameworks for safe, zero-downtime rollouts at any traffic volume.
Real-time drift detection, performance dashboards, and alerting pipelines that surface model degradation before it affects business outcomes.
A structured six-stage process from free MLOps assessment through full platform deployment and ongoing optimisation.
We audit your current ML workflows, identify bottlenecks in deployment and monitoring, and define clear success metrics for the MLOps platform. This session is free and carries no obligation. You leave with a concrete picture of what to build and why.
Our architects design the MLOps platform: CI/CD pipeline structure, model registry schema, feature store requirements, drift monitoring strategy, and cloud infrastructure plan. We select the right tooling for your use case and team.
We build a working CI/CD pipeline for one representative model to validate the architecture, surface integration issues, and demonstrate the end-to-end deployment and monitoring workflow before scaling to the full platform.
We build the complete MLOps platform, connecting pipelines to your data sources, model registry, serving infrastructure, and monitoring stack. Security controls, governance workflows, and audit logging are included from the first sprint.
We test pipelines against failure scenarios, validate drift detection thresholds, and verify that retraining workflows only promote models that have passed all evaluation gates. Load testing and security scanning are included.
Post-launch, we monitor platform and model performance, resolve production issues, and help your team plan the next iteration — including expanding pipeline coverage to additional models and use cases.
We audit your current ML workflows, identify bottlenecks in deployment and monitoring, and define clear success metrics for the MLOps platform. This session is free and carries no obligation. You leave with a concrete picture of what to build and why.
Our architects design the MLOps platform: CI/CD pipeline structure, model registry schema, feature store requirements, drift monitoring strategy, and cloud infrastructure plan. We select the right tooling for your use case and team.
We build a working CI/CD pipeline for one representative model to validate the architecture, surface integration issues, and demonstrate the end-to-end deployment and monitoring workflow before scaling to the full platform.
We build the complete MLOps platform, connecting pipelines to your data sources, model registry, serving infrastructure, and monitoring stack. Security controls, governance workflows, and audit logging are included from the first sprint.
We test pipelines against failure scenarios, validate drift detection thresholds, and verify that retraining workflows only promote models that have passed all evaluation gates. Load testing and security scanning are included.
Post-launch, we monitor platform and model performance, resolve production issues, and help your team plan the next iteration — including expanding pipeline coverage to additional models and use cases.
Select an industry to see how production MLOps reduces deployment friction and keeps models accurate in that domain.
MLOps enables financial institutions to deploy, govern, and continuously improve risk and fraud models without manual intervention or compliance gaps.
MLOps enables financial institutions to deploy, govern, and continuously improve risk and fraud models without manual intervention or compliance gaps.
Clinical AI requires rigorous governance, data locality controls, and validation pipelines that meet FDA and HIPAA requirements from day one.
Retail AI systems require high-frequency retraining, robust A/B testing, and the ability to roll back recommendation or pricing models instantly.
Predictive maintenance and quality control models must run reliably at the edge and in the cloud, with real-time alerting when model performance degrades.
Software companies need scalable multi-tenant model serving, feature stores, and LLM fine-tuning pipelines that keep pace with product iteration.
Legal AI systems require strict versioning, chain-of-custody logging, and governance workflows that satisfy enterprise legal and compliance requirements.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade MLOps engineering from point solutions and vendor-locked platforms.
We build audit trails, model lineage capture, and approval gates into every platform from the first sprint — not as an afterthought. Regulated industries including financial services, healthcare, and legal receive compliance-ready MLOps infrastructure that satisfies SOC 2, HIPAA, and sector-specific audit requirements.
We select the orchestration framework, model serving layer, and monitoring stack that best fits your use case — not the tools we are most familiar with. MLflow for experiment tracking, Seldon for serving, Airflow for orchestration, Evidently for drift: the right tool for each layer of the stack.
We design MLOps platforms that run on AWS SageMaker, Azure ML, GCP Vertex AI, or on-premise Kubernetes, and connect them to the data platforms and ERP systems your business already runs on. You are not locked into a single cloud or vendor.
Every engagement includes full documentation, runbooks, and team training so your data scientists and ML engineers can own and extend the platform. We build for your independence, not for a permanent dependency on Perimattic.
“Unlike vendors who sell you a platform without understanding your ML workflows, or consultants who advise without shipping, Perimattic brings strategy and engineering to every MLOps engagement.”
MLOps (Machine Learning Operations) is a set of practices that combines ML engineering, DevOps, and data engineering to reliably deploy and maintain machine learning systems in production. It automates the full ML lifecycle — from data preparation and model training to deployment, monitoring, and retraining. Without MLOps, teams spend most of their time on manual, error-prone processes rather than improving models. With MLOps, deployments become faster, more reliable, and fully auditable.
A basic MLOps setup — CI/CD for one or two models with automated testing and a simple model registry — typically takes four to eight weeks. Enterprise-grade implementations covering full CI/CD, drift monitoring, governance workflows, and a centralised feature store typically take three to six months. The timeline depends on the number of models in scope, your existing infrastructure, and the compliance requirements of your industry.
We work with MLflow, Kubeflow, Apache Airflow, DVC, Weights & Biases, Seldon Core, and BentoML — selecting the right combination based on your cloud provider, team expertise, and the complexity of your ML workloads. For infrastructure we use Docker, Kubernetes, and Terraform. We are tool-agnostic and always recommend the stack that best fits your use case, not the one we are most familiar with.
Even with two or three models, MLOps eliminates the 'works on my laptop' problem, provides a clear deployment process, and establishes the governance practices you will need as you scale. The return on investment increases significantly once you have five or more models in production, but the habits and infrastructure built early make that transition far easier. We scope every engagement to deliver value at your current scale rather than over-engineering for a future state.
DevOps addresses software code: build, test, deploy, and monitor applications. MLOps extends this to machine learning artefacts: data versioning, model training pipelines, model registries, drift monitoring, and automated retraining. The key difference is that ML systems degrade over time as real-world data distributions shift, even when the code is unchanged. MLOps adds the continuous monitoring and retraining loops that keep models accurate — something standard DevOps tooling is not designed to handle.
We implement statistical drift detection that continuously compares incoming production data distributions against the reference distribution from training time. When drift exceeds a configurable threshold, the system can alert the team, trigger a retraining pipeline automatically, or both. We use tools like Evidently AI, WhyLabs, and custom monitoring code depending on the complexity of the use case. Every drift alert includes a root-cause report so your team understands what changed and why.
Yes. We design MLOps platforms that integrate with your existing AWS, Azure, or GCP infrastructure. We work with AWS SageMaker, Azure ML, and GCP Vertex AI alongside cloud-agnostic tooling so you are not locked into a single vendor. If you have existing data platforms, data lakes, or ERP systems, we connect the MLOps pipelines to them as part of the engagement.
A feature store is a centralised repository that computes, stores, and serves engineered features consistently across model training and production inference. Without one, teams often compute the same features in multiple places, leading to training-serving skew — a common cause of model underperformance in production. We assess whether a feature store is justified for your current scale. For teams with multiple models consuming similar features or needing low-latency online inference, the investment pays back quickly.
Every MLOps platform we build includes a model registry with version control, approval workflows, and deployment authorisation gates. We capture full model lineage — which data was used, which hyperparameters were set, which training run produced the model — so you can reconstruct any production decision. For regulated industries such as financial services, healthcare, and legal, we add role-based access controls, immutable audit logs, and the documentation artefacts required for regulatory review.
Continuous training is the practice of automatically retraining a model when a trigger condition is met — scheduled time interval, data drift threshold, performance degradation below a baseline, or arrival of a labelled batch. It is most valuable for models where the data distribution changes frequently: fraud detection, demand forecasting, recommendation engines, and pricing models. We configure the triggers and validation gates so that a retrained model is only promoted to production once it has passed automated evaluation checks, preventing a degraded model from going live automatically.
Tell us about your ML deployment challenges and we will show you exactly where MLOps automation can reduce manual overhead, prevent model drift, and deliver measurable reliability improvements in the first quarter.