AI Use Cases
synvert Accelerators
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Every machine learning model can only show its value when it is used productively. We support you in going live with our expertise and many years of experience – from the initial architecture concept to the actual implementation in your system landscape.
Every machine learning model can only show its value when it is used productively. We support you in going live with our expertise and many years of experience – from the initial architecture concept to the actual implementation in your system landscape.
AI, data science and advanced analytics are driving digitalization, but their implementation poses challenges. Without high-quality data and a robust infrastructure, even the best models cannot realize their potential. This is where data engineering and MLOps come in, which together form the basis for data-driven solutions.
Data engineering ensures the collection, processing and provision of data. Using technologies such as Apache Kafka, Airflow, Spark and Snowflake, data pipelines are developed that integrate and transform data from various sources in real time or in batch mode, providing the basis for precise analyses and powerful machine learning models.
MLOps goes one step further by ensuring that AI and machine learning models are seamlessly integrated into production environments, continuously monitored and optimized. Tools such as MLflow, Kubeflow or TensorFlow Extended (TFX) support the automation and scaling of the model lifecycle. By combining data engineering and MLOps, companies not only create robust data solutions, but also sustainable AI applications that are flexible and future-proof.
Setup and operation of a machine learning platform.
Unified, scalable platform – a unified, scalable platform is at the heart of every project, whether in the cloud or on premise. This relieves the burden on data science teams, simplifies operation and maintenance and ensures that the exact resources required for effective machine learning operations are always available.
CI/CD pipeline – CI/CD pipelines ensure a reproducible, stable environment, simplify production implementation and avoid operating errors. A well-designed CI/CD pipeline offers considerable added value, especially for machine learning models with their typically diverse dependencies.
Model monitoring – machine learning models in production must be constantly monitored. This is the only way to recognize how good a model actually is and when a revision or re-training is necessary.
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