Every machine learning model can only demonstrate its value when it is used productively. We support you in the productive implementation with our expertise and many years of experience – from the initial architecture concept to the concrete implementation in your system landscape.
Every machine learning model can only demonstrate its value when it is used productively. We support you in the productive implementation with our expertise and many years of experience – from the initial architecture concept to the concrete implementation in your system landscape.
Developing sound machine learning and AI models is a challenge that requires experience, knowledge, and technical know-how. But an equally daunting challenge is moving these models from the developers’ systems into a productive environment, into everyday work. There are a number of points to consider here, from technical architecture to CI/CD to model monitoring.
Implementation and operation of a machine learning platform
Unified, scalable platform – A unified, scalable platform is at the core of every project, whether in the cloud or on premise. This eases the burden on data science teams, simplifies operations and maintenance, and ensures that the exact resources needed for effective machine learning operations are available at all times.
CI/CD - CI/CD pipelines ensure a reproducible, stable environment, simplify go-live and avoid operator errors. Especially for machine learning models with their characteristically diverse dependencies, a well-designed CI/CD pipeline offers considerable added value.
Model monitoring – Machine learning models in production must be monitored constantly. This is the only way to recognize how good a model actually performs and when a revision or re-training becomes necessary.
In a variety of projects in different industries, we at synvert saracus have already supported a large number of customers in the development of data analysis platforms and the productive implementation of machine learning models. Based on this, we have developed best practice frameworks, process models and reference architectures for on-premise and cloud environments (AWS, Azure, GCP). You can benefit from this wealth of experience in your project too.
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