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MLOPs: machine learning operations
The application of DevOps principles in ML systems enables large-scale solutions to be implemented and managed reliably and efficiently.
Context
Despite the increased adoption of models based on Machine Learning algorithms in recent years, companies have only been able in part to deploy solutions based on this type of technology into production, achieving a good return on investment.
Machine Learning Operations (MLOps) is a set of practices conceived with the aim of filling the gaps related to the integration and maintenance of these systems within companies' software architectures, based on the DevOps principles to facilitate the development, management and maintenance of these tools.
MLOps vs DevOps
DevOps is a software development methodology based on the principles of Continuous Integration and Continuous Delivery. Its purpose is to make development quicker and more efficient through frequent testing, integration and release cycles.
These practices are necessary but not sufficient for the development of software based on Machine Learning algorithms, for the following reasons:
Continuous Integration is not only about software components, but also about the underlying data and model
Continuous Delivery no longer concerns a single software package or service, but also the entire model training pipeline
a model needs to be re-trained over time
The notion of Continuous Training must therefore be introduced, meaning that we must automate the re-training of the model and the deployment of the new prediction service.
Our MLOPs approach
Thanks to project-based experience gained in recent years, as well as to scientific literature on the subject, we have refined our MLOps approach, summarised in the following key points.
MLOPs with Google Cloud
Google Cloud offers a range of services to meet the various needs that may arise during the development and deployment cycle of a Cloud product. As a certified Google Cloud Partner, our team recommends the following services for implementing the key principles described above.