Curriculum at a glance
The most up-to-date, effective tech stack on the market:
AI & Machine Learning Engineering Bootcamp
Do you want to master machine learning engineering from the ground up? Perfect! We’ll teach you the technical skills to build complex data architectures and efficiently integrate intelligent algorithms into real-world applications.
Full-time | 16 weeks | Remote
No money? No problem. Get 100% financing with the Agentur für Arbeit
Course Overview
This is what you learn in our AI & Machine Learning Engineering Bootcamp
Software Engineering
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.
Data Engineering
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.
MLOps: ML Engineering
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.
MLOps: Model Monitoring & Alerting
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.
Building AI Systems
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.
LLM Ops: Deploying AI Systems
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.
Capstone Project
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.
Programming
Learn Python, SQL, and object-oriented programming. Use Pandas and NumPy to manage and analyze data efficiently.
Data Analysis
Explore EDA, statistics, and feature engineering. Turn raw data into insights and prepare data for modeling.
Machine Learning
Apply regression, classification, decision trees, KNN, ensembles, clustering, and recommender systems to solve problems.
Deep Learning & NLP
Build neural networks with TensorFlow/Keras. Work with CNNs, transfer learning, and natural language processing with LLMs and AI agents.
Deployment
Use APIs, web scraping, and Streamlit. Deploy and monitor ML models with Docker, MLOps, MLFlow, and cloud pipelines.
Projects & Skills
Complete portfolio projects and capstones. Develop teamwork, agile workflows, communication, and data ethics for real-world impact.
Career Service
Ready for the real world.
What good are skills without getting a foot in the door? We focus extensively on helping you ace real world technical interviews.
Coaching built for you.
We believe that development is continuous, so we offer up-to-date career coaching sessions to help you progress professionally.
Beyond the technical.
Changing careers is more than learning new tech skills. We additionally provide you with spot on soft skills to ace your application process.
Your next step.
Wondering ‘what’s next’? We're connected with exciting startups and companies in Germany.
Where Spicedlings work
Spicedlings are getting hired by your favourite companies:
Financing Options
Invest in your future
Bildungsgutschein
If you’re registered as unemployed (or soon to be) in Germany, you could be eligible to have all your costs covered with a Bildungsgutschein (training voucher).
For more information on this option, check our page dedicated to financing your coding bootcamp with a Bildungsgutschein.
Deferred Payment
We want to make our best-in-class tech courses available to everyone with the motivation to complete them.
Our Deferred Payment Option enables those who aren’t in the position to pay upfront nor in instalments to participate, by offering the chance to pay back at a later date.
Full Payment
If you’re ready to cover the cost of our coding bootcamps immediately, this is the option for you. Pay 14 days before the course starts.
FAQ
How does SPICED Academy support students during the Data Science and Machine Learning bootcamp?
At SPICED Academy, we're dedicated to your success. Throughout the bootcamp, our experienced instructors and mentors will guide you, answer your questions, and provide valuable feedback tailored to advanced programming. Our learning environment encourages independent exploration, ensuring you get the most out of your AI Engineering journey with us.
I'm new to AI. Is this a problem for Data Science and Machine Learning bootcamp training?
Our course is designed to take you from an intermediate level to beyond. If you're new Data Science and AI, be sure to check out our Data Science & AI program.
What sets SPICED apart from other AI Engineering - Data Science and Machine Learning bootcamps?
SPICED stands out by focusing on hands-on experience and preparing you for real-world AI engineering scenarios. Our supportive community creates an environment where you can excel and thrive as a developer. We emphasize practical skills and industry relevance, equipping you with the necessary tools for success in programming beyond the classroom.
What are the average salaries for AI Engineers in Germany?
In Germany, AI engineer salaries vary based on factors such as location, experience, and skill level. On average, experienced professionals can command salaries ranging from €60,000 to €80,000 per year.
I’m based in Berlin—what’s realistic salary and job roles I can achieve after finishing the AI Engineering Bootcamp?
Excellent local perspective—Berlin leads in AI startups and scaleups. Entry-level roles and earnings typically include:
1. AI Engineer / ML Ops Associate: €50,000–€65,000/year
2. Machine Learning Engineer (entry-level with deployment experience): €60,000–€75,000
3. Generative AI Engineer: €55,000–€70,000 depending on employer and specialization.
Job pathways: Fintech, adtech, healthcare AI startups; big firms using SageMaker, Deep Learning pipelines, or custom models in production.
💬 “I completed the AI Engineering Bootcamp part-time while job searching. Two months after graduation, I started working at a Berlin AI startup making €62k in a full-stack AI engineer role.”
✅ Bootcamp job support, internships during the course, and open-source project visibility help push your path faster into Berlin’s AI ecosystem.
I don’t have a computer science degree—can I still succeed in an AI Engineering Bootcamp?
Absolutely! In fact, many AI Engineering Bootcamp participants come from non-traditional backgrounds—marketing, economics, logistics, even psychology. What matters most is your willingness to learn and solve problems logically.
🧠💪 Here’s what bootcamps typically expect:
Basic Python: Loops, functions, list comprehensions, and libraries like NumPy or pandas. Analytical mindset: Being able to follow structured logic and troubleshoot step-by-step. Grit: You'll hit bugs and barriers—your persistence is key.
No CS degree? No problem. Bootcamps are designed to bridge that gap fast:
- They offer pre-course prep kits to level up coding.
- You’ll build real-world projects (not theory-heavy research).
- AI Engineering is less about academic AI and more about building and deploying systems.
Testimonial: “I worked in HR before the bootcamp. I finished with three deployed models and got hired as a Junior AI Ops Engineer in six weeks.”
✅ If you’re self-motivated, ready to learn cloud and ML deployment, and can commit time, your background won’t hold you back.
What thousands of Spicedlings say
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