Curriculum at a glance
The most up-to-date, effective tech stack on the market:
AI Engineering Bootcamp
Push your boundaries. Explore how AI engineering seamlessly combines data science and machine learning to tackle complex web architectures. Master the latest AI technologies with our Data Science and Machine Learning Bootcamp course.
Full-time | 6 Months | Remote
No money? No problem. Get 100% financing with the Agentur für Arbeit
Design your path. Drive your product.
PythonFastAPIDockerMLOpsdbtscikit-learnTensorflowSQLPrefectGrafanaPrometheusPandas
Step beyond your comfort zone and transform from an aspiring AI engineer to an inspiring one. Master data science and machine learning with the most comprehensive curriculum on the market. 🚀
Course Overview
What's in store at our AI engineering bootcamp
Phase 1: Coding & Software Engineering for Data
Lay a strong foundation in the technical tools and programming skills that data scientists need to manipulate data, build workflows, and communicate effectively.
Phase 2: Exploratory Data Analysis (EDA)
Discover the essential methods and tools to analyze, clean, and visualize data, extracting insights to inform decision-making.
Phase 3: Machine Learning Fundamentals
Develop foundational machine learning skills, focusing on supervised learning and key models.
Phase 4: Deep Learning
Build a strong foundation in deep learning, exploring neural networks and modern architectures to tackle complex tasks such as image classification.
Phase 5: Statistics
Develop the analytical rigor needed to interpret data reliably, validate assumptions, and support strategic business decisions with statistical evidence.
Phase 6: Advanced Data Science & AI
Dive into more advanced data science methods to handle unstructured data and complex patterns.
Phase 7: Unsupervised Learning
Unlock hidden structures in data to identify customer segments, optimize strategies, and generate actionable insights without predefined labels.
Phase 8: Data Products + Data Science capstone project
Apply everything you’ve learned so far in a 4-week capstone project, transforming data insights into real-world data products and delivering end-to-end solutions to a business case.
Phase 9: Software engineering for Data Science
Master the foundations of writing production-ready Python code, version control, and deployment with Docker and cloud tools.
Phase 10: Data Engineering
Build robust data engineering skills to source, model, and pipeline data for scalable, real-time applications.
Phase 11: Machine Learning Engineering
Master the machine learning lifecycle and deploy reliable data science products end-to-end.
Phase 12: Model monitoring and Alerting
Develop robust processes to ensure long-term reliability of ML models in production with robust monitoring, drift detection, and automated retraining
Phase 13: Deployed Data Products
A hands-on project where students apply machine learning engineering concepts to solve a real-world business problem. Students will design, deploy, monitor, and maintain ML models, ensuring they meet performance and scalability requirements. The project involves end-to-end model development, from data preprocessing and training to deployment and real-time monitoring. Teams will collaborate to create production ready solutions, simulating the responsibilities of machine learning engineers in a professional environment.
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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