The actual performance of an AI development environment - flexibility in the development and training of models, cost efficiency, data security, reproducibility, rapid deployment, efficient collaboration, monitoring and maintainability - often only becomes apparent during the course of the project.
Indeed a whole lot of tools are available today that help with the development of AI models. Keeping track of the development, selecting components wisely, and integrating them seamlessly is not an easy task.
Here at Ambrosys, fiercely good data scientists team up with seriously skilled software engineers to pursue the mission: Build infrastructures in which your AI ideas can become productive.
Get your AI the environment it deserves:
Book a free consultation with our AI infrastructure experts.
What clients say about Ambrosys
“Ambrosys provided us with this development landscape that gave the process structure, for which I am very grateful. It allowed us to further develop our models in a safe space. This is particularly important in Germany and Europe because of the GDPR.”
Jeremy Bormann, Founder, Legal-Pythia
“We've learned an awful lot from Ambrosys, simply because someone showed us how to do it properly.”
Martin Eckstein, CTO Lumics GmbH
“How can you train software effectively and know how good it is at any point during training? How can you compare different methods? How can you send something to the supercomputer that you previously developed on a laptop? Their strength is connecting the individual packages with each other. It's not an easy task because the individual components are dynamic and evolve.”
Peter Düben, Head of Earth System Modelling
Questions and Answers
If any of the following applies, you should be uncompromising with the architecture of your development environment:
- Your business opportunity requires quick scaling.
- Your ML application is your main source of competitive advantage.
- Your ML operations need to be integrated seamlessly into your other systems.
- You are subject to elevated security or data protection standards.
- Your application processes large amounts of data.
- Your data is heterogeneous or from many different sources.
- You need to deploy to a variety of different systems.
- Your trained models shall be optimized or re-trained on a regular basis.
Any of your favorite tools can be integrated into environments we build, whether data storage, library, computing resource, containerization routine or deployment pipeline. Connectivity and smart interfaces are bread and butter work to us.
We don’t discourage you to do so if you feel able. Many of our clients appreciate the comfort and flexibility that comes from buying in AI development and operations expertise, however, or prefer to focus on building up domain know-how, instead of worrying about the intricacies of data and software engineering.
We are constantly involved in top-class academic and industrial research projects in order to keep our finger on the pulse of the latest developments. It is our job to make research actionable for innovative business models.
There are various ways to make your leap of faith a little easier. E.g. we start with a lightweight consulting project to design the concept - paid by the hour. Then, we complete an MVP at a fixed price. Several shared-risk options are possible in the case of long-term cooperation.