AI Operations
MLOps Platform Setup
End-to-end MLOps platform design and implementation — tool selection, pipeline design, and team enablement so your organization can operationalize AI with confidence.
An MLOps platform is the foundation that determines whether your organization can move models from experiments to production reliably, repeatedly, and at scale. Without one, every deployment is a custom project. With the right platform, deployment becomes a disciplined, repeatable process.
TrustEdge designs and builds MLOps platforms that match your organization's maturity level, team capabilities, and compliance requirements. We do not impose a one-size-fits-all architecture. We start with your current state, understand where you are headed, and build a platform that grows with you.
Every platform we build includes compliance-integrated pipelines, experiment tracking, model governance, and monitoring — because in trust-critical industries, these are not optional add-ons. They are the baseline. And we train your team to run the platform independently, because a platform that requires consultants to operate is not a platform — it is a dependency.
What's Included
A complete MLOps platform tailored to your models, your team, and your compliance requirements.
Platform Architecture Design
Design an MLOps platform architecture that fits your team size, model complexity, and compliance requirements — from simple pipelines to enterprise-scale infrastructure.
Tool Selection & Integration
Evaluate and select the right tools for experiment tracking, feature stores, model registries, and deployment pipelines. We recommend based on your context, not vendor relationships.
Pipeline Design & Implementation
Build end-to-end ML pipelines from data ingestion through model training, validation, deployment, and monitoring. Reproducible, testable, and compliant by design.
Feature Store Setup
Design and implement feature stores that serve both training and inference, with versioning, lineage tracking, and access controls appropriate for regulated data.
Experiment Tracking & Reproducibility
Set up experiment tracking that captures hyperparameters, metrics, artifacts, and environment details. Every experiment is reproducible and auditable.
Team Enablement & Documentation
Train your data science and engineering teams to operate the platform independently. We deliver comprehensive documentation and hands-on workshops — not just a handoff email.
How We Work
A phased approach that delivers value early and ensures your team owns the platform when we are done.
Discovery & Requirements
We interview your data science, engineering, and compliance teams to understand workflows, pain points, compliance requirements, and future growth plans.
Architecture & Tool Selection
We design the platform architecture and recommend tools based on your requirements, existing infrastructure, team capabilities, and budget constraints.
Foundation Build
We implement the core platform — infrastructure provisioning, CI/CD pipelines, experiment tracking, and model registry — with your team involved at every step.
Pipeline Development
We build production ML pipelines for your most important models, establishing patterns and templates that your team can replicate for future models.
Team Onboarding
We run hands-on workshops where your team builds and deploys models using the new platform. We stay available for questions during the first month of independent operation.
Who This Is For
Organizations Starting Their MLOps Journey
Teams that have models in notebooks and need a platform to move them to production reliably. We help you skip the common mistakes and start with a solid foundation.
Growing AI Teams
Organizations scaling from a few models to dozens that need standardized pipelines, shared infrastructure, and consistent governance across the portfolio.
CTOs & Engineering Leaders
Leaders who need to invest in ML infrastructure that supports growth, maintains compliance, and does not create vendor dependency.
Trust-Critical Industry Enterprises
Healthcare, financial services, and legal organizations where the MLOps platform must integrate compliance controls from day one — not bolt them on later.
Results Our Clients See
faster model deployment
10x faster model deploymentexperiment reproducibility
100% experiment reproducibilityplatform operational
< 12 wk platform operationalvendor lock-in
Zero vendor lock-inTechnology Partners
Related Capabilities
Frequently Asked Questions
How long does a full MLOps platform setup take?
A typical platform build takes eight to sixteen weeks depending on scope. We deliver in phases — core infrastructure in the first four weeks, pipeline development in weeks five through ten, and team enablement in the final phase. Your team starts using the platform well before the full engagement ends.
Which MLOps tools do you recommend?
It depends entirely on your context. We work with MLflow, Kubeflow, SageMaker Pipelines, Azure ML, Weights & Biases, DVC, Feast, and many others. We evaluate tools against your specific requirements — team size, model complexity, compliance needs, and existing infrastructure — rather than defaulting to a standard stack.
Can you extend our existing MLOps platform rather than building from scratch?
Absolutely. Many engagements start with an existing platform that needs specific improvements — better monitoring, governance integration, or pipeline optimization. We assess what you have, identify the highest-value improvements, and implement them incrementally.
How do you ensure the platform works for both data scientists and engineers?
We involve both groups in the design process and optimize for their different workflows. Data scientists get experiment tracking and notebook integration. Engineers get CI/CD pipelines and infrastructure-as-code. Governance and monitoring serve both teams. The platform bridges the gap rather than forcing one group to adapt to the other.
What cloud providers do you support?
We build platforms on AWS (SageMaker, EKS, Step Functions), Azure (Azure ML, AKS, Azure DevOps), GCP (Vertex AI, GKE), and hybrid/multi-cloud environments. We are certified partners with AWS and Microsoft, and we design platforms that avoid vendor lock-in wherever possible.
Do you provide ongoing platform support after the initial build?
We design platforms for your team to own and operate independently. After the initial engagement, we offer optional quarterly reviews where we assess platform health, recommend improvements, and help with scaling challenges. But the goal is always your independence, not our ongoing involvement.
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