Education voucher webinar
AI Engineering: March 2026 GraduationMar 27, 2026
The final project presentations of our new AI Engineering graduates!
Education voucher at hand? Step into the fast lane: Contact us
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AI Engineering - Data Science and Machine Learning Bootcamp
New
Next seat available
Apr 27, 2026
You want to learn AI engineering? Perfect! Here you'll get all the skills you need to build effective machine learning systems in the real world in hands-on projects.
100%
funding available
18
different programs
70%
Labour market integration
7K+
> 7k alumni
10+
Years of experience
Course Content
All content at a glance
Keyfacts
- Full-Time: 32 weeks (Mo – Fr, 09.00am – 6.30pm)
- Participants: approx. 15
- Locations: Remote (live online)
- Coaches: 2 per bootcamp
- Course language: English
- Completion: Data Science Certificate and Machine Learning Engineering Certificate
- Future job: Machine Learning Engineer, Data Scientist
- Expected salary: 60.000€ - 90.000€
- 100% financing: for unemployed & job seekers
Our coaches
Anastasia Mikheeva
Head of Data Science
Tech Stack
Python
Docker
DBT
Tensorflow
SQL
Prefect
Grafana
Prometheus
Scikit
Pandas
FastAPI
MLOps
Download tech stacks
Become an AI Engineer - at the neue fische bootcamp
Stop analyzing data in isolation. Start building and deploying intelligent systems. The future of tech belongs to engineers who can bridge the gap between data science and production-ready software. At neue fische, you get a unique, dual-certification program that takes you from the fundamentals of Data Science to the development, deployment, and monitoring of data products.
Hands-On Training for a Future-Proof Career
This program combines solid theory with practical content, fully preparing you for a new career trajectory. You will learn by doing, mastering the tools that power the data industry:
➡️ Tool mastery: Work on real-world projects and master tools like Python, TensorFlow, Scikit-learn, SQL, DBT, Prefect, Prometheus, Grafana, Pandas, and Jupyter Notebooks.
➡️ Portfolio power: by working on two Capstone projects, you will build a strong portfolio that captures the attention of leading companies.
Career launchpad: Thanks to our comprehensive career support, you will have strong chances to succeed in your new professional direction.
Why AI Engineering ?
Dual Mastery for Maximum Earning Potential. This combined program is a strategic investment that positions you as having the best chance to succeed in the data world. If you have a background in research, this is where you will transform your deep analytical skills into a highly practical, business-focused domain. And if you have a technical background, this is where you will master the advanced techniques of model deployment, monitoring, and software engineering - the skills needed to move into a core "builder" role.
Our partner companies
Starting dates
The next dates: AI Engineering - Data Science and Machine Learning Bootcamp
✅ The AI Engineering - Data Science and Machine Learning programme will be fully remote.
Apr | 27th Apr – 23rd Dec ‘26 | Full-Time | Remote | English | Secure seat |
|---|---|---|---|---|---|
Jun | 29th Jun – 23rd Feb ‘27 | Full-Time | Remote | German | Secure seat |
Jun | 29th Jun – 23rd Feb ‘27 | Full-Time | Remote | English | Secure seat |
Curriculum
This is what you learn in our AI Engineering - Data Science und Machine Learning Bootcamp
Students are introduced to the overall structure and expectations of the course. They learn how to organize their learning, manage time and stress, and develop a mindset that supports professional growth. An initial overview of the IT industry helps them understand their future work environment, while the first steps in career development and personal branding help set long-term goals. A dedicated session on how to responsibly use AI tools prepares students to integrate AI thoughtfully into their learning. Technical setup ensures everyone is ready to get started.
Students will learn the basic set of skills needed to work in a unix shell. They also recap on general programming Python language fundamentals and write and execute their first small programs. By using Git and Github, they are introduced directly to modern collaborative work in the industry, including code versioning, as well as working collaboratively on the codebase with others through the use of concepts such as branching and pull requests.
Building upon the fundamentals of Phase 1, students are introduced to tools for extracting and manipulating data from different sources, like files but also databases. To do this they use SQL and Pandas. After practicing data manipulation skills they dive into creating descriptive analysis using various plotting libraries and plot types. This phase is completed with a 2 days project focussed on Exploratory-Data-Analysis (EDA) where students work on a real-world dataset. Always with the business case in mind, they provide their artificial stakeholder with recommendations and meaningful visualizations tailored to their needs.
This section covers the basic concepts of supervised machine learning techniques like regression and classification. Students are introduced to multiple algorithms among others linear and logistic Regression and Decision Trees. Beside introducing the algorithmic and statistical details of various machine learning algorithms, we have the scope and assumptions of the modes in mind. We emphasize the importance of model evaluation and which tradeoffs one have to have in mind when building predictive models. Students apply all of their learned skills by working in groups on a 4 day machine learning project that covers the whole data science life cycle. Together they set milestones and consider the value of their data product, while gaining experience in collaborative work with Git and Github. The final step is a presentation to the stakeholders.
With a basic understanding of statistical learning techniques and the underlying software implementation, students dive deeper into techniques for forecasting on Time Series. In a world with mostly unlabeled data, it is important to tackle also the challenges of unsupervised learning. Students will get an introduction to both dimensionality reduction and clustering techniques. To get some understanding of concepts that are nowadays often referred to as AI, students get an introduction to artificial neural networks. Building their own deep neural network from scratch will improve their understanding of the underlying processes and concepts. Also typical areas of application like Natural Language Processing and common related techniques like transfer-learning will be addressed.
To condense all the skills learned and also focus even more on group work and collaboration, the capstone project gives the context and time to work on a bigger data science problem from start to end. Students have the chance to find a problem by their own, using publicly available datasets or work with data provided by one of our partner companies. In teams of 3 to 4 people they work towards achieving and presenting a solution to the given problem. Thereby, not only the gained knowledge regarding technical topics and agile methodologies will be applied, but they will also extend their skills based on the requirements of their projects.
Bridge the gap between data science notebooks and production-ready code. Focus on clean code, object-oriented programming (OOP), and testing.
Finalize software foundations with Docker and begin Data Engineering by learning to move and model data reliably.
Master workflow orchestration and understand the difference between batch and real-time data processing.
Transition from modeling to production. Learn behavioral testing and deployment strategies for ML systems.
Automate the delivery of ML software using Docker and Continuous Integration/Continuous Deployment (CI/CD) pipelines.
Ensure production systems stay healthy by monitoring service performance and model degradation.
Simulate a full MLOps cycle and begin the transition into Large Language Model (LLM) Engineering.
Communicate effectively with LLMs and build Retrieval-Augmented Generation (RAG) systems.
Optimize LLM performance using Fine-Tuning (LoRA/QLoRA) and Hybrid RAG architectures.
Build autonomous agents that use tools and reason through complex tasks using standard protocols.
Finalize agentic workflows and learn to serve LLMs as scalable APIs.
Deploy LLM applications to the cloud and implement specialized monitoring for GenAI.
Kick off the final project. Define the problem, set success metrics, and build the data acquisition pipeline.
Transform data into features, train baseline models, and present early results.
Automate the training workflow and finalize the production architecture.
Finalize CI/CD, documentation, and present the complete end-to-end system.
Simple and affordable
Education must be affordable. Check out all the financing options now.
Education voucher
Agentur für ArbeitPay directly
No problemPayment by the employer
Re- & UpskillingEducation must be affordable. And for everyone. That's why we offer three ways how you can finance your bootcamp with us, guaranteed and very easy.
These steps are important to take the course
Whether you have dropped out of university, are on short-time work, are threatened with unemployment or have lost your job, your time will be well spent if you become more digital. Please note that the process of getting an education voucher from the employment agency can take a relatively long time. We therefore advise you to follow the procedure below.
Step 1
Register as a jobseeker early
In order to receive your education voucher for your retraining from the employment agency, the Jobcenter or the Labour Office, you should register as a jobseeker at an early stage. It is therefore very important that you first make an appointment with the relevant office. It's best to do it now!
Step 2
Get your educational offer from us
The next step on the way to your IT training voucher is quick and easy: Contact us! We will create an official training offer for you that you can then submit to the employment agency, the Jobcenter or the employment office.
Step 3
Apply for the training voucher
Now it's down to the nitty-gritty: With the training offer we have created, you now go back to your responsible office and apply for your training voucher. As soon as it is approved, you can start your new career with us. We look forward to seeing you!
FAQ
Really good questions, helpful answers
You still have questions about the training, the prices, the financing, etc.? Then take a look here or contact us directly.
It's designed for all entry levels. There're no diploma or technical prerequisites required for this course, whatsoever. You will need a Google Account.
However, if you’re using a work computer, we recommend checking with your IT department to ensure you have access to Slack and Zoom.
It’s for anyone who wants to be part of a cohort of tech enthusiasts with the same ambition: to succeed, excel, and grow together. 🚀
Studying in a cohort is all about collaboration. You'll progress alongside your peers, and autonomously build your hard skills while also sharpening your teamwork abilities and soft skills. The best part: You're never doing it alone!
Just the basics. A stable internet connection and a computer. We recommend you to have a camera and microphone access as well.
After the bootcamp, you can, for example, start out as a AI engineer, machine learning engineer or data scientist.
Yes, don't worry! Many participants start from scratch, and AI bootcamps are designed to guide you step by step into more complex topics. But some preparation helps immensely:
Python basics: Including data types, loops, functions, and libraries like NumPy – 4–6 weeks of self-study lays the foundation.
Linear algebra & probability: Basic understanding of matrices, vectors, sigma, and normal distributions – online courses like Khan Academy offer a good starting point.
Machine learning intro: Free training with Scikit-Learn pipelines or Kaggle beginner tutorials – so you don't fall behind in the classroom.
Test your computing environment: Create a free Google Colab or Azure Notebooks account – this will familiarize you with cloud development.
Remote careers are particularly popular and in demand in the AI field. Here's how to position yourself optimally:
Remote setup: Ensure your course covers tools like GitHub, Docker, Kubernetes, cloud services (Azure, AWS, GCP), and CI/CD pipelines – this is what remote employers expect.
Certifications for visibility: Certificates like Azure AI Engineer, AWS Certified Machine Learning, or TensorFlow Developer strengthen your remote market value.
Open source contributions: Actively showcase your own AI projects, AI demos, or Kaggle notebooks on GitHub – words speak louder.
International networks: Join global Slack channels like AI Engineering Slack, LinkedIn AI groups, or Kaggle forums – this will help you gain references and job leads.
Remote advantage: Share your communication skills in your application – Zoom, documentation practices, and async communication skills demonstrate your suitability as a remote engineer. 💼🌍
This positions you as a future-proof AI engineer – regardless of country or city, with global reach and free agent freedom.
What are you waiting for?
Our Student Admissions team is happy to talk with you, answer your questions, and advise you. Get in touch with us!
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