MLOps Maturity Scan
Our MLOps Maturity Scan evaluates your team’s current maturity level and identifies key areas for improvement.
how it works
It begins with an intensive, face-to-face workshop, and within just 2 weeks, we’ll deliver a comprehensive MLOps maturity assessment of your team dynamics, tools, and processes, along with actionable insights.
Key deliverables of the MLOps Maturity Scan
Key recommendations
Detailed analysis
Tailored roadmap
Understand the steps required to optimize your MLOps processes, ensuring scalable and reliable AI solutions.
How to?
Our proprietary MLOps maturity model is the cornerstone of all MLOps practices at Superlinear, and the foundation of our workshops.
The Superlinear MLOps Capability Maturity Model (CMM) Framework consolidates proven principles for building and maintaining AI solutions efficiently and effectively. It enables us to bring best practices to every AI project, ensuring success from the ground up.
Our Machine Learning Engineers and Solution Leads are fully trained in the CMM framework and bring extensive hands-on experience from previous projects.
The framework helps answer three critical questions about your MLOps maturity:
Where am I situated today?
Assess your current MLOps maturity level.Which problems should I solve, and in what order?
Define your "Build Order" for prioritizing tasks.Which solutions should I pick for each problem?
Build your ideal MLOps Stack tailored to your needs.
For each pillar, we can distinguish five MLOps maturity levels:
Level 1 – Manual: Model development and deployment is fully manual and has limited documentation or tracking.
Level 2 – Repeatable: Repeatable model development: others can repeat a documented process, which brings improved consistency and quality to the result.
Level 3 – Reproducible: Fully reproducible model development: the result itself is exactly reproducible, which enables both efficient collaboration as well as low-effort maintenance.
Level 4 – Automated: Automated model development and deployment: brings increased efficiency for the team and organization.
Level 5 – Improving: Model development, model performance, and maintenance is optimized to bring continuous improvement.
By zooming in on each pillar, we define a stack to tackle the problems in the Build Order. The framework acts as a comprehensive checklist, ensuring all MLOps challenges are addressed without overlooking critical issues.
Backed by our experience across AI domains and countless projects, we offer predefined stacks tailored to common project settings (e.g., AI domains, cloud providers). These serve as a solid starting point, while being fully customized to fit your team’s specific needs.
Once your MLOps stack is defined, the Framework becomes a high-level summary of your team’s MLOps maturity, workflow and roadmap. A powerful tool to align stakeholders and improve your processes over time.
Whether you’re struggling with data quality, model deployment, or operational inefficiencies, our MLOps maturity assessment provides a roadmap to accelerate your AI initiatives and achieve scalable, reliable, and high-performing solutions.
If you have no AI solutions in production, the workshop serves as a robust foundation for your AI journey. We’ll help you start on the right foot and efficiently define a build order and stack for your team.
If you’re struggling with reliability, reusability, or self-service, we’ll help you pinpoint pain points and make a course correction with 3-5 key recommendations.
If you’re looking to refine your processes or adapt to organizational changes or new projects, we’ll provide a second opinion on your current practices and suggest improvements tailored to your new challenges.