From countering cyber attacks and ensuring sustainable use of resources, to optimising business processes – artificial intelligence (AI) creates innovative solutions. When used correctly, AI offers significant competitive advantages. From Software as a Service (SaaS) solutions, to custom machine learning models – with Ergon by your side, your AI solution will be tailored to your specific situation and thus create genuine added value. Together, we can turn your idea into a productive, scalable and reliable solution.
Step by step to perfection: the path of AI from measurable KPIs to machine learning
Analysing the potential
Together, we will refine your business case and define clear KPIs to make the business benefits of the AI solution quantifiable.
Selecting an approach
Whether it involves developing proprietary machine learning models, using foundation models or integrating SaaS solutions – we will find the right approach for you.
Training and optimising models
We optimise AI models precisely to suit your specific situation by means of training, transfer learning, finetuning or prompt engineering.
Industrialising the solution
To make sure that a model will work reliably under real conditions, we establish DataOps and MLOps methods. This makes the models scalable and futureproof.
Providing support in the long term
During operation, we monitor any changes in data (concept drift), carry out retraining if necessary and make the necessary adjustments.
“Clear metrics are needed for both machine learning and mathematical optimisation. This is the only way to create an algorithm that can solve the relevant problems.”
From theory to practice: our integrated approach for AI, machine learning and deep learning
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Focuses on the benefits for your business
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Cutting-edge integration of the latest technologies, models and methods
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Independent development for any environment, whether on the edge or in the cloud
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Ethically works with data and AI to come up with solutions to help people and the planet
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Provides sustainable and reliable solutions thanks to DataOps and MLOps
Turn your AI vision into a reality: have a non-binding chat with our experts about your AI ideas and projects to find out more about what we can offer.
FAQs on AI learning, machine learning and deep learning
Can I still benefit from machine learning consulting if I only have a vague idea of what I want?
Of course! Our experts are on hand to assist you whatever stage you are at with your project. Whether you just have an idea in mind, have already gained some experience with existing models and products, or have trained AI models yourself, we help you develop the perfect productive AI solution to meet your needs.
Do I need vast amounts of data for the machine learning to work?
Fortunately, there are lots of different options available nowadays, so you can use ML models even if you do not have large amounts of data. For many ML problems, there are what are known as ‘foundation models’ that have already been pre-trained for general tasks like speech, image or text processing. Many of these models are open source or available from all major cloud providers. We optimise these types of models to ensure they can solve your domain-specific problem in the best possible way. Depending on the model, we use methods such as transfer learning, finetuning or prompt engineering. Since these approaches use far less data than the initial foundation model training, we can develop an ML model for you using just a small, but high-quality, data set.
How can I create a high-quality data set for my solution?
We help you to collect high-quality, consistent and relevant data. ‘Quality, not quantity’ is our motto when it comes to data-centric machine learning. We provide the support you need for data collection and attach a great deal of importance to data protection, data security and compliance.
How can we improve existing machine learning models in our company and adapt them to new challenges?
Possible approaches include: expanding and diversifying training data, checking and optimising hyperparameters, implementing transfer learning, avoiding overfitting through regularisation and analysing sources of errors in models. Overfitting is when a machine learning model only thoroughly learns its training data and cannot, therefore, handle new data. Regularisation helps in this situation by giving the model general patterns to learn instead of unimportant details.
What role does model interpretability play when introducing machine learning in companies?
Interpretability is very important, particularly in regulated industries or when ML-based decisions have an impact on people. Technologies like explainable AI (xAI) open up the black box and make the logic behind predictions fully traceable.
How can we scale machine learning projects and transfer them to production?
It is important that you have a robust ML infrastructure and automated training and deployment processes. But you must also be able to integrate ML projects into existing systems and workflows and monitor model performance and continuous learning.
