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AI Engineering - Data Science and Machine Learning Engineering Bootcamp
New
Next seat available
Jun 29, 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.
15+
Different tech-powered courses
69%
Hiring rate
(within 1 year)
(within 1 year)
7K+
More than 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: 62.000€ - 90.000€
- 100% financing: for unemployed & job seekers
- You'll get a Claude Pro subscription during the bootcamp
Our coaches
Dr. Tereza Iofciu
Department Head Data Science + Machine Learning Engineering
Tech Stack
Python
Docker
dbt
Tensorflow
SQL
Prefect
Grafana
Prometheus
scikit-learn
Pandas
FastAPI
MLOps
Download tech stack
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 Engineering Bootcamp
✅ The AI Engineering - Data Science and Machine Learning Engineering programme will be fully remote.
Jun | 29th Jun – 23rd Feb ‘27 | Full-Time | Remote | German | Secure seat |
|---|---|---|---|---|---|
Jun | 29th Jun – 23rd Feb ‘27 | Full-Time | Remote | English | Secure seat |
Aug | 31st Aug – 30th Apr ‘27 | Full-Time | Remote | German | Secure seat |
Curriculum
This is what you learn in our AI Engineering - Data Science and Machine Learning Engineering 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 2, 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 focused 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 for regression and classification. Students are introduced to multiple algorithms, like linear and logistic regression, decision trees, and ensemble methods, among others. Besides introducing the statistical details of the algorithms, we also consider the scope and assumptions of these models. We emphasize the importance of model evaluation and which tradeoffs one has to keep 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.
In the first phase students will become familiar with software engineering practices and how they relate to data science. The objective of the first week is to write better code when working with data science projects.
In order to achieve this we will cover software engineering in Python (writing programs, working with git and object-oriented programming). Then we will show how to bridge the gap between the usual data science workflow and production-ready code.
At the end of this phase students will be comfortable with getting data for their models from many different sources in different formats. Data engineering is about moving and transforming data from one place to another in a reliable and trustworthy way. Students will get introduced to data architecture design for batch and real-time data processing. They will learn how to get data from various sources like database access and APIs. They will then learn the concepts of data modeling with dbt. Following that they will build data pipelines with Prefect and learn the concepts of batch processing and streaming. Finally, they will set up a feature engineering pipeline in the cloud for their Data science project.
In the third phase of the bootcamp the students will get familiar with the machine learning lifecycle and how to bring data science products to production. There will be an introductory session on machine learning basics followed by sessions on testing, deployment strategies, and containerization.
In this phase of the bootcamp students will get familiar with what it means to have machine learning products in production working reliably over time. In the previous phase, they learned how to deploy models; now they will learn how to monitor and maintain them.
By the end of this phase , students will be able to understand, build, evaluate AI systems using modern Large Language Model (LLM) technologies. They will develop foundational knowledge of LLM architectures, embeddings, vector search, and prompt engineering; gain hands-on experience constructing Retrieval-Augmented Generation (RAG) pipelines; perform fine-tuning and evaluation of small models; and design custom agentic systems using frameworks such as LangChain, pydanticAI, and MCP.
In this phase, students will learn to deploy and manage LLM applications. They will build FastAPI services, containerize them with Docker, and deploy AI systems to Google Cloud Run. Students will integrate monitoring and observability to ensure reliability, and they will learn to deploy complete RAG and agent pipelines end-to-end.
In the final phase of the bootcamp, students will take on a comprehensive capstone project that brings together everything they’ve learned. They’ll design, build, deploy, and monitor a complete machine learning system that solves a real-world problem. Working in teams, students will operate as a professional MLE group, using best practices from software engineering, data engineering, machine learning engineering, model monitoring, and LLM development. The bootcamp concludes with a presentation and live demo of their solution to instructors and peers.
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? 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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