ML Engineering Foundations
Master the fundamentals of machine learning engineering with hands-on Python, TensorFlow, and MLOps training. Build production-ready ML systems from day one.
Comprehensive ML Engineering Foundation
Our foundational program transforms software developers into production-ready ML engineers through practical projects and industry-standard practices.
What You'll Master
Python for Machine Learning
Master NumPy, Pandas, and Scikit-learn for data manipulation, feature engineering, and model building with real-world datasets.
Deep Learning Frameworks
Build neural networks with TensorFlow and PyTorch, covering CNNs for computer vision and RNNs for natural language processing.
MLOps Fundamentals
Deploy models using Docker, manage experiments with MLflow, and implement CI/CD pipelines for ML systems.
Cloud Integration
Deploy models on AWS SageMaker and Google Cloud AI Platform with hands-on workshops and real infrastructure projects.
Program Benefits
- Weekly code reviews with senior ML engineers
- Google ML Professional certification preparation
- Portfolio of 8 production-ready ML projects
- Direct access to European ML job market
- Alumni network access across DACH region
Career Transformation Results
Our foundations graduates consistently achieve remarkable career growth and salary increases
"The foundations program gave me exactly what I needed to transition from traditional backend development to ML engineering. The hands-on projects and industry mentorship made all the difference."
"Perfect balance of theory and practice. The cloud integration workshops prepared me for real-world deployment challenges I face daily in automotive ML systems."
Professional Tools & Technologies
Master the same tools and frameworks used by leading ML teams across Europe
Python Ecosystem
NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn for data science workflows
Deep Learning
TensorFlow, PyTorch, Keras for neural network development and training
Cloud Platforms
AWS SageMaker, Google Cloud AI, Azure ML for scalable model deployment
MLOps Stack
MLflow, Docker, Kubernetes, Git for ML lifecycle management
Professional Development Environment
Cloud Computing Resources
Access to powerful GPU clusters for training deep learning models with enterprise-grade infrastructure
Industry-Standard IDEs
JupyterLab, VS Code, PyCharm Professional licenses for optimal development experience
Real-World Datasets
Access to anonymized enterprise datasets from Swiss banks and German automotive companies
Safety Protocols & European Standards
Our program adheres to strict GDPR compliance and European AI ethics frameworks
Data Protection & Ethics
GDPR Compliance Framework
All student projects follow European data protection regulations with proper anonymization and consent management protocols.
Ethical AI Development
Training includes bias detection, fairness metrics, and responsible AI practices aligned with EU AI Act requirements.
Secure Development Practices
Code security scanning, vulnerability assessment, and secure model deployment practices for production environments.
Quality Assurance Standards
Perfect For Aspiring ML Engineers
Designed for professionals ready to transition into the machine learning field
Software Developers
Experienced programmers looking to add ML capabilities to their skill set. Perfect for backend developers, full-stack engineers, and DevOps professionals.
Data Professionals
Data analysts, business intelligence developers, and statisticians seeking to advance into machine learning engineering roles.
Technical Career Changers
Professionals from engineering, science, or technical backgrounds looking to pivot into the rapidly growing ML field.
Who Succeeds in Our Program
Progress Tracking & Results Measurement
Comprehensive assessment system ensuring mastery of ML engineering fundamentals
Continuous Assessment Framework
Weekly Code Reviews
Senior ML engineers review your code for best practices, efficiency, and maintainability standards used in production environments.
Project Portfolio Development
Build 8 increasingly complex ML projects, from basic classification to end-to-end cloud deployments with monitoring.
Technical Interviews
Mock interviews simulating real hiring processes at European tech companies, focusing on ML system design.
Skill Progression Tracking
Certification & Credentials
ML Academy Certificate
Industry-recognized completion certificate with detailed skill verification
Google ML Certification
Preparation and exam voucher for Google Cloud Professional ML Engineer certification
Project Portfolio
Professional GitHub portfolio showcasing 8 production-ready ML projects
Explore Our Other Programs
Continue your ML engineering journey with advanced specializations
Production ML Systems
Advanced program focusing on scalable ML architecture, distributed training, and enterprise-grade deployment strategies for high-volume production systems.
Learn MoreCareer Transformation
Immersive bootcamp with guaranteed job placement, industry mentorship, and direct recruitment pipeline to top tech companies across the DACH region.
Learn MoreStart Your ML Engineering Journey Today
Join our next foundations cohort starting August 12, 2025. Early bird pricing ends July 30th.