Models in production lose accuracy over time, and no one detects it early enough. Results gradually deteriorate, and the problem becomes apparent only after it affects decisions, processes, or the user experience.
AI Operations (MLOps)
Control, monitor, and scale your AI models in production.
We automate deployments, monitor performance, and manage changes so your models operate with control, traceability, and lower risk.
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We are a Microsoft Top Partner
What happens when AI models reach production without a defined operating model
Model deployments are performed manually. Every production release depends on steps carried out by people, increasing operational risk, extending delivery times, and making it difficult to roll back a change when something fails.
There is no traceability for model and dataset versions. When it is necessary to explain which version produced a result or which data was used for training, the answer is unavailable, making audits difficult.
Changes to data or prompts produce unexpected behavior, creating business risk. At the same time, multiple teams develop models without shared standards, complicating governance across the portfolio.
How we operate your models throughout their lifecycle
We assess how your models are currently developed and operated, design the MLOps architecture, automate deployments with CI/CD for AI, implement quality, performance, and usage monitoring, and define version and change management.
Greater reliability in production
Shorter deployment time
Better traceability
Early detection of degradation
AI lifecycle assessment
We assess your model development and operations processes. Deliverable: maturity report.
MLOps architecture design
We define the pipelines and operating models that support the lifecycle. Deliverable: target architecture.
Deployment automation and monitoring
We implement CI/CD for AI and monitor model quality, performance, and usage. Deliverables: automated pipelines and an operational dashboard.
Governance and traceability
We manage versions and changes across models and components. Deliverable: operating framework.
Technology stack
Each technology plays a defined role: Microsoft Fabric for data lifecycle operations and governance, Azure as the infrastructure for training and deployment, GitHub for version control and CI/CD automation, and Azure Monitor for observability and operational monitoring.
How we work
Each operating model is implemented through the Possumus Adoption Framework, our four-stage adoption framework. This ensures that the work does not end with automated pipelines: It continues with ongoing operations and improvement across your models' lifecycle.
1
Prepare
We assess your current model operations and the maturity of your processes. This stage produces the maturity report.
2
Incorporate
We design the MLOps architecture and automate deployment pipelines with CI/CD for AI.
3
Impact
We implement monitoring for model quality, performance, and usage. This stage produces the operational dashboard.
4
Scale
We sustain the lifecycle through operations and continuous improvement, with version and change management documented in the operating framework.
What your organization gains
A defined AI operating model makes your models' lifecycle repeatable and scalable, improving the sustainability of your AI initiatives.
Reliable models in production
Greater reliability of models in production helps reduce business risk.
Faster delivery of AI capabilities
Shorter deployment times accelerate the delivery of new AI capabilities.
Audit-ready compliance and traceability
Better traceability across model versions, data, and changes facilitates audits and compliance.
Degradation detected early
Early detection of degradation helps prevent operational impacts before they affect the business.
Why Companies Choose Possumus
Proven Expertise Across the Microsoft Ecosystem
Our specialization as a Microsoft Solutions Partner demonstrates our ability to design and implement solutions that integrate with each organization’s technology ecosystem.
Engineering Integrated with Adoption
We combine engineering and adoption from the start so technology is not only implemented, but also incorporated into operations, used, and continuously evolved over time.
A holistic view of technology
We connect AI, data, security, software, and cloud under a single vision, avoiding isolated solutions and reducing technology fragmentation.
Audited Management Processes
Our operations are supported by processes audited under ISO 9001.
Frequently asked questions
The most common signs are models in production losing accuracy over time, manual deployments, a lack of traceability across model and dataset versions, changes to data or prompts producing unexpected behavior, and multiple teams developing models without shared standards. Each of these situations exposes the business to degraded results, operational risk, or audit and governance challenges.
Maintain the performance of your AI models in production
Assess my AI operationsWe start with an assessment of your current operations to define the MLOps architecture your models need.
We Are a Microsoft Solutions Partner
We hold designations across all Microsoft Cloud solution areas.
Microsoft Cloud
6 Specializations
- Adoption and Change Management
- Custom Solutions for Microsoft Teams
- Meetings and Meeting Rooms for Microsoft Teams
- Secure AI Productivity
- Microsoft 365 Copilot
- Analytics on Microsoft Azure
3 Specializations
- Identity and Access Management
- Threat Protection
- Microsoft 365 Copilot
4 Specializations
- Analytics on Microsoft Azure
- Agentic DevOps with Microsoft Azure and GitHub
- Infra and Database Migration
- Azure Virtual Desktop
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Team Certifications
Let's Get In Touch
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Federico Tagliani
Managing Director
Our work doesn’t end with implementation. We make sure the technology is adopted and delivers results.