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 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.