MLOps

AIGard builds the pipelines that deploy, monitor and retrain machine learning models, so they keep working after launch. We bring versioning, automation and governance to ML.

The work typically covers:

• CI/CD pipelines for models
• Model registry and versioning
• Automated training and deployment
• Drift and performance monitoring
• Scheduled and triggered retraining
• Governance and audit documentation

MLOps is usually the right investment in one or more of these situations:

• Models take weeks to reach production
• No one knows which model version is live
• Model accuracy degrades unnoticed
• You need audit trails for regulated use

On a 30-minute scoping call we confirm whether it fits your case and recommend a team shape and first milestones.

Yes, AIGard can add MLOps around models that are already in production. We start by putting existing models under version control and monitoring, then automate deployment and retraining.

Starting with versioning and monitoring establishes which model version is live and whether its accuracy is holding. Automation follows through CI/CD pipelines and scheduled or triggered retraining, with governance and audit documentation added where models are used in regulated settings.

AIGard works with MLflow, Databricks, Vertex AI and cloud-native services, and fits into the MLOps tooling you already run.

Our engineers work inside your tools and environment, and we review the existing setup before recommending what to keep or replace. Supporting tools such as Kubernetes, Docker, Airflow, GitHub Actions and Terraform are used where they suit your stack.

By treating the model as one part of a system. Production work covers data pipelines, evaluation, deployment, monitoring and retraining as well as the model itself.

We agree the success metric first, assess whether your data supports the use case, and build the surrounding engineering so the system keeps performing after launch.

Privacy and security controls for each AI use case are defined up front, and engineers work inside your environment under least-privilege access.

We agree which AI tools and models may access your code and data, including retention and training settings, with human approval for sensitive actions.

For MLOps, AIGard engineers typically work with MLflow, Kubernetes, Docker, Airflow, Databricks, Vertex AI, GitHub Actions and Terraform.

The stack is selected to fit the systems you already run, and engineers are matched to it before they join.

MLOps is available through any of AIGard’s three engagement models: staff augmentation, where engineers join your team; a dedicated team that runs the workstream; or software outsourcing, where AIGard delivers a defined project.

The choice depends on how much of the delivery you want to manage, and it can change later under the same agreement.

Engineers typically start within two to four weeks of the scoping call. The exact date depends on the roles, seniority and number of people required.

We confirm a realistic start date in writing before you commit, and you meet the proposed engineers before work begins.

AIGard engineers typically cost 30–50% less than the fully loaded cost of a comparable US-based hire, with no recruiting fees, benefits overhead or insurance costs on your side.

The figure for a specific engagement depends on the model, the roles, the seniority mix and the size of the team. We issue a written proposal after the scoping call, so the commercial terms are agreed before any work starts.

NDAs are signed before any access is given, and engineers work inside your environment under least-privilege access, with permissions limited to their responsibilities.

Access is reviewed as roles change and removed when work ends. Our practices align with ISO 27001, SOC 2, GDPR and HIPAA, with PII masking, encryption and audit logging built into data work. We review your access policies, data-handling needs and vendor requirements during scoping.

You do. Intellectual property assignment is written into every contract, in every engagement model.

That covers source code, designs, documentation, data, models and prompts produced for you. Third-party components and their licenses are identified, so ownership is clear at handover.