Machine Learning Operations – MLOps, for friends – is a term that describes principles and techniques to establish an even level of automation, collaboration, and efficiency to Machine Learning as we have reached for classical software development over the last years.
At Cloudflight, we developed a maturity model to assess the current state of maturity within an organization, determining the next steps to take towards an advanced MLOps adoption.
MLOps explained
Machine Learning has revolutionized the way we approach complex problems and make decisions. However, building and deploying Machine Learning models at scale can be a challenging and time-consuming task. This is where MLOps comes into the picture.
MLOps, or Machine Learning Operations, is a set of principles and practices aimed at bringing the same level of automation, collaboration, and rigor to the development and deployment of Machine Learning models as we have in classical software development. By adopting MLOps principles, organizations can streamline their machine learning workflows, increase the speed and quality of model development, and ultimately achieve better business outcomes.
In this article, we will explore how to assess the maturity of MLOps adoption in your company – to give you an evaluation of where you stand and hint you towards possible next steps to take or where to improve.
The MLOps maturity model
The more we rely on Machine Learning (ML) and Artificial Intelligence (AI) technologies, the more establishing a professional development and operations practice becomes crucial. An MLOps maturity model can help organizations assess their current level of MLOps, identify areas for improvement, and develop a roadmap for achieving their desired state. That’s why even big tech companies such as Google (see documentation) or Microsoft (see documentation) have defined their MLOps maturity models.
Important aspects to consider are:
- Standardization and Automation: Standardization and automation are essential for scaling ML systems effectively. An MLOps maturity model can help organizations define and implement standard processes and best practices for ML development, testing, deployment, and monitoring at the right level. Automation can help streamline these processes, enabling teams, reducing the risk of human error, and improving overall efficiency.
- Collaboration and Communication: Collaboration and communication between different roles and teams involved in the ML lifecycle are critical for success. An MLOps maturity model fosters clear communication channels as well as role definitions and responsibilities for each team. This ensures that everyone is on the same page and has a shared understanding of the ML system’s requirements and goals.
- Continuous Integration and Delivery (CI/CD): Continuous integration and delivery (CI/CD) practices are essential for delivering not only ML models quickly and reliably, but software in general. DevOps principles help organizations establish and implement automated testing and deployment pipelines, reducing the time and effort required to get new models into production.
- Monitoring and Governance: Monitoring and governance are critical for ensuring that ML systems remain reliable and effective over time. An MLOps maturity model can help organizations establish monitoring and governance processes that detect and address issues such as model drift early, ensuring that ML models remain accurate and effective in the long run.
In conclusion, an MLOps maturity model supports organizations that want to scale their ML operations effectively. By focusing on the aforementioned aspects, organizations can develop mature MLOps practices that drive innovation and business value.
Conclusion
In our MLOps maturity model, we define 4 levels of MLOps principles and methods adoption. Starting from not having implemented any, via sticking to DevOps principles, we approach full MLOps facilitation by adding tools and automation to the ML development and deployment workflows.
The maturity model is a useful means to assess an organization’s current state of ML operations. Important steps to base upon such an evaluation are to be aware of the next reasonable principles to adopt, to consider them from a cost-/benefit-point of view, and evolve your organization where reasonable.
To trigger change processes, or to select adequate tools, you can either do your own research and evaluations or seek support from ML experts.
Here we are displaying our model.
It includes a questionnaire to assess your maturity level, plus a detailed description of every level.
