Artificial intelligence & machine learning
AI strategy and ML engineering
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Transformation with AI
Together we explore key concepts, starting with the AI strategy that will help your organisation harness the transformative power of new technologies.
We cover the entire field from machine learning engineering to ML Ops, which is the basis for efficient and scalable implementation of machine learning projects.
For example, we get to know the fascinating world of generative AI. One part of this is Natural Language Processing (NLP). Here, large language models (LLM) are trained using machine learning (ML). You will also learn that pre-processing is crucial to transforming raw data into meaningful information.
Are you ready to open the door to a future with artificial intelligence? We accompany you on an exciting journey of discovery with the following services relating to AI and ML:
- AI strategy and use case management
- Data Science & Pre-Processing
- ML Engineering (Training processes, Optimisation, Integration, Testing/QA, UX-Design)
- Computer Vision and Deep Learning
- MLOps (DevOps, (cloud) infrastructures for machine learning, automation)
Example
Generative AI solutions for companies
The exponential growth of generative AI is significantly changing the world of work and impacting companies worldwide. This trend is reflected in reports by McKinsey, Forbes and the World Economic Forum and is also reflected in the strategic decisions of leading technology companies. Here we present a portfolio that can help organisations keep pace with and take advantage of rapidly evolving technologies.
A well thought-out AI strategy defines clear goals, identifies relevant use cases and seamlessly integrates AI into the corporate vision.
The centrepiece of a successful AI strategy is use case management. The aim here is to identify specific areas of application for AI that offer measurable added value for the company. This requires a thorough analysis of existing business processes to identify potential areas where AI algorithms and models can be used.
Structured use case management makes it possible to set realistic expectations, use resources efficiently and maximise the ROI (return on investment). This also includes the selection of suitable AI technologies such as generative AI, with e.g. Natural Language Processing (NLP) or topics like computer vision that optimally match the identified use cases.
AI strategy and use case consulting
Data science and pre-processing: the basis for data-driven innovation
Data Science und Pre-Processing bilden das Fundament für den Erfolg von Analysen und Erkenntnissen aus großen Datenmengen.
Data science, as an interdisciplinary science, combines statistical methods, machine learning and advanced analysis techniques to gain valuable insights from data. The key to this lies in pre-processing, a critical phase in which raw data is cleansed, structured and prepared for analyses.
Pre-processing includes steps such as the removal of incorrect data, the handling of missing values and the conversion of data into the required format. A uniform basis is created through normalisation and standardisation. This process is crucial to improve the quality of the data and ensure reliable results in the subsequent analysis phases.
ML Engineering
ML engineering is crucial for the development of machine learning systems.
It includes training processes, optimisation, integration, testing/QA and UX design.
The training process aims to recognise and generalise patterns in data. Optimisation continuously improves the accuracy and efficiency of models. Integration requires seamless adaptation to existing systems. Testing/QA ensure reliability and robustness. UX design is important for presenting results in an understandable and effective way.
ML engineering requires a comprehensive approach for robust and effective systems.
MLOps
MLOps combines machine learning with DevOps, integrates best practices and automates the entire ML lifecycle.
The use of (cloud) infrastructures enables scalable resources. Automation plays a key role, accelerates the development process and improves the deployment of ML models. The agile combination of MLOps, DevOps and (cloud) infrastructures optimises efficiency and reliability across the entire ML lifecycle.
Our comprehensive computer vision technology is carefully tailored to your specific requirements. From dynamic real-time solutions to customisable and optimised products, we offer a comprehensive range of services:
For example, we use state-of-the-art deep learning algorithms for advanced human motion analyses to improve safety measures in the field of autonomous driving.
In the areas of supply chain, logistics and production, we optimise operational processes through activity recognition and model precise perception methods for automated quality control.
Examples of projects in the field of computer vision:
AI protection
ATTENTION funding project
Computer Vision & Deep Learning
Improved pedestrian detection with computer vision
A reliable process for the holistic evaluation of the various AI functions was developed for a verifiable safety argumentation.
Targeted training of the AI with modern simulation technologies has improved pedestrian recognition even under difficult conditions.
Example of pedestrian detection to set up and illustrate the chain of reasoning
Testing of existing algorithms for pedestrian detection
Identification of critical scenarios (corner cases), e.g. altered incidence of light or temporarily obscured view
Workshop
Use cases for companies
podcast
Retirement tsunami – what is it?
I Want Change Podcast, Season 2, Episode 3: What happens to companies when many people retire at the same time? Does the knowledge then also leave the company or are there enough successors with the same level of knowledge? Demographic change has been foreseeable for years – but what about the consequences?
Katharina talks about this with her colleagues Markus and Manuel in the third episode. What dangers, opportunities and processes play a role here? A little teaser: it’s off to the world of artificial intelligence.
SUCCESS STORIES
Customer references in the field of ML Engineering
DATEV AI workshop
As part of the DATEV AI workshop, QualityMinds was able to develop experimental prototypes for AI-supported applications for the DATEV IT cooperative. These offer DATEV customers a safe space to try out AI in the form of a platform, facilitate work processes and promote joint learning with regard to AI.
Demographic outflow of knowledge
To meet the imminent challenge of the ‘retirement tsunami’, QualityMinds was able to develop a multi-layered concept based on AI and LLMs, among other things. This means that companies and organisations can even emerge stronger from the upcoming transformation.
Ready to take off with us?
Do you have questions, want more information or simply have suggestions? Then write to us, we look forward to hearing from you!
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