Year after year, every company generates vast amounts of data in a wide variety of activities. Systematically unlocking and leveraging this treasure trove of data is both a challenge and a great opportunity. Data Science projects and the development of Machine Learning and AI models help your company gain new insights and business opportunities
The buzzwords AI, data science and advanced analytics have enjoyed great popularity in the business world for several years. However, the challenges and difficulties become apparent in everyday work when real existing data is used to solve real existing requirements and use cases of different departments. Developing good machine learning and AI models is a challenge that requires experience, knowledge and technical know-how.
Data Science and the development of Machine Learning models can simplify and automate a wide range of tasks in everyday business. Some examples from past projects:
Time series analyses – workload prediction and thus more precise use of resources, churn prediction, early detection of non-obvious trends
Text analysis and NLP – classification and routing of incoming documents, determination of prioritized processing, complaint forecasting, social media monitoring
Information extraction from documents – opens the way to fully automated transaction processing
Anomaly detection – preventive maintenance of machines, fraud detection.
Image analysis – automatic defect detection, object tracking, OCR/text extraction
Classification algorithms – Recommendation systems/Next Best Offer, risk assessment, market analysis.
At synvert saracus, we have realized a wide range of Data Science projects in a variety of industries. Our process models, best practices and the software modules we use are therefore tried and tested. We can provide support in the implementation of complex data analyses, the development of forecasting or classification algorithms, complex AI models and many other topics.
The success of a data science project or the usefulness of a machine learning model depends on several factors. Relevant factors include the quantity and quality of available training data, the required complexity of the model, business requirements and, last but not least, achievable savings and efficiency gains. We support you in identifying the most promising use cases and in concretely planning the implementation.
One of the key components of any successful model is the careful compilation and preparation of the training or input data. Our Data Engineers and Data Scientists can either perform this data preparation for you largely on their own or provide your team with advice and support.
Developing a powerful machine learning model is a combination of science, experience and intuition (and carefully prepared data). This is where you can benefit from our diverse experience. We have already implemented many use cases in a similar fashion and can therefore select and implement the most promising algorithms and models.
A Data Science project has only reached its goal when the developed system is used productively and can thus prove its value. We can also support you in this final step with our experience from a wide range of projects, from system architecture to implementation and further development.
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