Mapping MLOps Maturity Levels into Reality
At Roche we have a goal to deliver twice as many medical advances to society at half the cost. Artificial Intelligence is a key driver in order to achieve this and teams of MLOps engineers are emerging to turn this into reality. Leveraging Google MLOPs maturity levels is helping our teams speak a common language and achieve the same goals, mapping them has proven challenging and closing the gap to achieve one of these levels is non-trivial. Clearly prioritizing the risks associated with not doing MLOPs helps organize which gaps to fill first and help deliver value
*Machine Learning is key in supporting Roche’s aim of delivering twice as many medical advances to society at half the cost.
*MLOps maturity levels help bring awareness and architectural guidance when building production-ready Machine Learning models.
*Identifying risks helps prioritize which component needs to be implemented first when gaps are identified between Google MLOps maturity levels and status quo.
Oswaldo Studied Physics at UNAM with a focus on Computational Physics and MScin Big Data Science at UCA. He is an experienced professional currently working as a Senior IT Professional - MLOps Engineer at Roche in Poland. He is passionate about the intersection between computing infrastructure and Artificial Intelligence. With experience in the IT, Financial and Pharmaceutical industries he has a wide spectrum of the different challenges that need to be taken into account when exposing ML in production. He is inspired by his current role since he can contribute back to society in a more direct manner by helping accelerate drug discovery from his MLOPs engineering trench. He works with Kubernetes, Kubeflow, DKube, Python, AWS, ArgoCD, Gitlaband constantly looking for new cloud-native technologies that can help bridge the gap to production while minimizing maintenance. Part of a novel team of MLOPs engineers at Roche with a common passion and a clear goal.