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Model Deployment & MLOps

Put your machine learning into production—securely, reliably, and with peace of mind. We automate, monitor, and support your models end-to-end.

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Why Invest in Model Deployment & MLOps?

Building models is just the start. We ensure your machine learning delivers value—live, on time, and at scale. Our MLOps solutions automate deployment, monitoring, and retraining so you stay ahead of the competition and avoid costly surprises.

From cloud APIs to on-prem or edge, we tailor MLOps for your real-world needs and compliance demands.

Where MLOps Delivers Value

Real-Time & Batch Prediction

Serve up live predictions to users or run batch scoring jobs—seamlessly and reliably, wherever your data lives.

Example: An online lender deploys a loan approval model via cloud API for instant credit decisions, while a hospital uses batch pipelines to score patient risk every morning.

Model Governance & Compliance

Track model versions, audit decisions, and automate monitoring for drift, uptime, and bias—meeting regulatory and business needs.

Example: A bank implements version control and monitoring for its risk models, passing audits and reducing operational risk.

Automated Retraining & CI/CD

Keep your models current with automated pipelines for retraining, testing, and deployment—so your predictions stay accurate as data evolves.

Example: A retailer sets up nightly retraining for demand forecasts, reducing manual work and improving inventory planning.

API, Batch, and Edge Deployments

Package models for any environment: cloud, on-prem, mobile, or IoT devices. Integrate with business apps or workflows for seamless prediction delivery.

Example: A logistics provider deploys route optimization models to edge devices, enabling real-time decisions even offline.

Our Deployment & MLOps Process

  1. Free Discovery Call: Share your models and deployment needs. We’ll show options, examples, and pricing—no commitment.
  2. Environment Health Check: We review your production or staging environment, model artifacts, and integration requirements—flagging risks early.
  3. Packaging & Deployment: We containerize or package your models for the target environment, deploy them, and provide APIs, batch jobs, or edge packages.
  4. Monitoring & Automation: We set up dashboards, alerts, CI/CD pipelines, and retraining to keep your models reliable, compliant, and up-to-date.
  5. Validation & Handover: You get documentation, monitoring dashboards, compliance logs, and ongoing support contacts.
  6. Optional Ongoing Support: We offer monitoring, incident response, upgrades, and enhancement as your needs change.

What You Need to Get Started

  • Trained model(s) ready for deployment (Pickle, ONNX, TensorFlow, etc.)
  • Access to production or staging environment
  • IT/DevOps or business sponsor involvement
  • Willingness to sign a mutual NDA (your models and infra are always protected)

Not sure if you’re ready? Most organizations can deploy faster than they expect—book a free consult and we’ll guide you through the process.

Starter Pricing

  • Pre-consult & Discovery: Free
  • Typical deployment/MLOps project: $2,500 – $6,000

Contact us for a tailored quote—every deployment is designed for your environment and compliance needs.