Successful implementation of advanced analytics
Developing intelligent business applications with data science and machine learning.
From data to competitive advantage
Data science and machine learning methods can identify complex, previously unknown patterns even in a large volume of data. This enables predictions to be made allowing:
- Optimisation of complex business process
- Automated decision-making
This generates a significant competitive advantage. We can help you develop a viable solution with our technological and methodological experience around artificial intelligence and machine learning methods: right from Proof of Concept (PoC) to a productive and scalable solution.
Frequently asked questions on the way from PoC to marketable solution
What are my ideas and goals? What is it I want to know, find out or be able to predict? (Automatic detection of defects, for example).
Is the data suitable for solving the problem or answering my question?
Are algorithms suitable for solving the problem?
After an initial analysis of the technical framework (cloud or local): Is it possible to implement my idea in this way? (Example: I have images of defective plant and want to check whether machine learning methods can be used for automatic defect detection).
Which other potential use cases can I implement with the data?
Start small - think big: Idea to machine learning PoC
At a glance: Added value through the use of advanced analytics and machine learning methods
The challenges of machine learning as a value-added technology – examples from the manufacturing and automotive sectors:
Predictive maintenance in practice
The research division of a plant manufacturer has developed a machine learning model to predict the energy output and service life of wind turbines. The company now faces the challenge of releasing this model to the market as quickly as possible, as a productive solution that can also be used to efficiently analyse large volumes of data from several thousand turbines.
Computer vision in practice
Recognition of lane markings is an essential element on the way to autonomous driving systems. This is possible via image data and Convolutional Neural Networks (CNNs). It is no longer possible to imagine image processing in the field of deep learning without these tools. The result is a trained model capable of detecting and recognising lane markings in an image.
NLP in practice
Chatbots are digital assistants that can be used to optimise internal processes and to automate and optimise support processes. AI and machine learning – especially natural language processing (NLP) – are at the technological heart of this. A simple application example is an early warning system for incident reports, which automatically detects clusters of certain incidents from historical and current data and supports the user in terms of initial countermeasures.
Achieve your goal with doubleSlash: Successful implementation of your advanced analytics project
As a consulting and software company, we cover the entire range of services from data science strategy and integration right through to data analysis and data visualisation. And we’re also pretty good at data engineering. Thanks to our many years of experience in designing and implementing complex IT projects, we’re the perfect partner to lead your advanced analytics project and machine learning to success. Here’s what we can offer you:
- Checking of existing AI solutions or PoCs
- Evaluation of suitable technologies such as Python libraries, cloud environments (AWS, Azure)
- Use of standard frameworks (BERT, for example)
- Use case implementation with Python, Tensorflow or Matlab
- Structured analysis of data quality regarding text or image recognition
- Economic feasibility study (ROI calculation)
- Product support (market analyses, business model consulting)
Results and artefacts:
- PoC: lean testing of market feasibility
- Basis for evaluating the potential of your data and feasibility of your use cases
- Comprehensive decision template to drive the project forward within the company
- Recommendation of the appropriate technologies and methodologies stack
- Learn and apply: benefiting from the experience of methodology and technology experts
- Automated business processes to facilitate forecasting and automated data-driven decisions
What we bring to the table:
We are familiar with all the relevant machine learning methods (especially NLP, computer vision, predictive maintenance) and will help you select and use suitable advanced analytics technologies.
From the DIY workshop to productive operation: We have solid experience implementing software projects and are not afraid of large, unstructured and fast-moving data (Big Data).
Our highly trained data science experts use their in-depth mathematical and statistical know-how to determine which algorithms and methods are best suited to your specific advanced analytics use case - and implement them accordingly.
Best practices in chatbot development
In order for a chatbot to be useful as a smart assistant, the concept has to be properly planned from the start. But how can a chatbot provide a company with concrete added value, and what technologies are used? From concept to realization: find out about our best practices here.
Machine learning methods in practice
The guide to successful machine learning projects