Trainings

We offer technical trainings for specialists such as software engineers, software architects or data scientists on how to build and run productive AI and machine learning solutions.

Two students discussing a learning topic displayed on a laptop

Our Offering

We have extensive experience in building and operating AI and machine learning (ML) solutions in enterprise environments. We observed that AI and ML solutions generally have a higher degree of complexity than more traditional software solutions, and it requires a mix of data science and software engineering skills to address this complexity appropriately. Our trainings target this intersection - a space increasingly referred to as ML engineering.

We offer our courses in two formats:

Scheduled sessions via trusted third-party training providers,
On-demand, customized courses held on-site at client locations.

Besides our technical trainings we also offer various workshops for executives and managers, including (but not limited to):

Awareness and introduction workshops such as promptathons for text and image generation,
AI-based Design Thinking workshops.

Have a look at our services page for more details.

Topics covered

Solution Design & Architecture for AI and ML Systems

AI and ML systems often require special attention on model development and usage as well as handling data flows. AI and ML models are directly derived from datasets through training, and as such they represent a hybrid between data and executable code. It is for this reason that the design and architecture of AI and ML systems is often more complicated than for traditional software systems. Operational aspects like model drift monitoring or periodic model re-training need to be planned upfront.

Document Processing

Document processing is usually the first, necessary step to building large-scale natural language processing (NLP) and AI systems. In our trainings students learn what the common challenges and solutions for document processing in modern enterprises are including for example optical character recognition (OCRing), table extraction, or how to handle form data.

Natural Language Processing & Understanding

Mastering natural language processing (NLP) and understanding (NLU) techniques is a necessary complement to building high-quality document and text processing systems. While large language models have decreased the initial hurdles to build NLP/NLU solutions significantly, they do have their own drawbacks such as limited context size windows or simply high processing costs. In our course we learn how to use NLP/NLU and LLM systems side-by-side to make use of the best of both worlds as needed.

Retrieval Augmented Generation (RAG) & LLM Chatbots

Retrieval Augmented Generation (RAG) allows to build powerful document search engines and chatbots that can give customized answers not only on generic knowledge about the world but specifically on one's own corpus of documents. Students learn how to systematically design, build, optimize and operate RAG systems and chatbots.

Agentic AI

Agentic AI is an architecture style that takes automation of existing work processes to the next level. "Agents" are software processes with a predefined level of autonomy, that allows them to reason about a situation and react accordingly taking autonomous decisions within closely defined boundaries. While agents have existed for a long time already the advent of large language models has acted as fuel for this architecture style once again.

MLOps & LLMOps

Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps) are the natural extensions of the DevSecOps idea applied do ML and AI systems. In our courses we introduce students to the principles and available tools for creating scalable and secure ML and AI systems.

Could not find the training you are looking for?

Reach out to us to discuss your needs. We can provide a variety of training courses and upskilling programs tailored to the needs of our clients.