AI Use Cases
synvert Accelerators
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Machine Learning Operations (MLOps) is a platform-defining solution that covers the entire ML lifecycle – from exploration, data import, pre-processing, training and prediction to post-processing and export. It enables scenario simulations, the combination of ML models, automated re-training, monitoring and alerting and offers a flexible, standardised environment for development and operation with support for all ML libraries.
Machine Learning Operations (MLOps) is a platform-defining solution that covers the entire ML lifecycle – from exploration, data import, pre-processing, training and prediction to post-processing and export. It enables scenario simulations, the combination of ML models, automated re-training, monitoring and alerting and offers a flexible, standardised environment for development and operation with support for all ML libraries.
Machine learning and artificial intelligence are changing the way companies make decisions – just as the internet revolutionised communication. While initial investments in ML projects are already showing signs of success, new use cases and opportunities are constantly emerging. However, scaling ML remains a challenge: the path from exploration to productive use is long, and as the number of use cases increases, so does the technological diversity and the complexity of the infrastructure. Companies are struggling with traceability, quality assurance and the challenge of integrating multiple ML models into consolidated predictions. In addition, maintenance requires highly specialised personnel who are not available for more strategic tasks.
The solution lies in Machine Learning Operations (MLOps) and Process Simulation Infrastructure. MLOps ensures automation, standardisation and versioning of ML processes – similar to DevOps for software development. A process simulation infrastructure also enables the optimisation of ML pipelines before they go live, thereby reducing risks and costs. Our MLOps Accelerator supports companies in scaling ML applications more efficiently and sustainably so that they can not only implement individual pilot projects, but also establish machine learning as a strategic success factor.
Defining standards for processes, development, code, data, artifacts and metadata information
Standardized interfaces and structures allow for an automatisation of most processes and the creation of generic patterns and modules that are reuseable
An automated process is easy to use, doesn‘t forget anything and can also be automatically documented and well understood by new-comers
If processes are transparent, it becomes easy to define quality gateways, monitoring, alarming and automated retrainings/executions to ensure consistent quality
Our MLOps Accelerator offers a complete development, test and deployment environment for the entire ML process. It supports optimised model training with hyperparameter tuning, various ML frameworks and complex scenario simulations. Automatically collected metadata from the training phase is used for predictions, while integrated scheduling automates (re)training and predictions. Structured logging and monitoring at use case level ensure transparency and reliable process monitoring
Our platform makes it easy to control, monitor and analyse workflows and create workflows with building blocks based on the LEGO principle. Programming skills are largely not required for end users. Functionality and implementation are separate, which enables simple updates and customisations. Back-end development is handled by the platform team, while users only need to fill in templates for import, export, prediction and training tasks.
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