Production ML Systems
Master enterprise-grade ML architecture, distributed training, and real-time inference systems that serve millions of users daily.
Enterprise-Grade ML Architecture
Advanced specialization for experienced ML engineers building scalable systems that handle millions of predictions per day across distributed infrastructure.
Advanced Technologies You'll Master
Kubernetes for ML
Deploy and orchestrate ML workloads with Kubeflow, manage GPU clusters, and implement auto-scaling for training and inference pipelines.
Distributed Training
Master Apache Spark for ML, implement data parallelism with Horovod, and optimize distributed deep learning across multiple GPUs and nodes.
Real-time Inference
Build low-latency prediction APIs with TensorFlow Serving, implement model caching strategies, and optimize for sub-100ms response times.
A/B Testing Frameworks
Design statistically robust experimentation platforms, implement multi-armed bandits, and measure model performance impact on business metrics.
Advanced Program Benefits
- Direct mentorship from Google and Meta engineers
- Access to real enterprise datasets from partners
- Build 5 production-scale ML systems from scratch
- Advanced Kubernetes and AWS ML certifications
- Senior ML engineer job placement pipeline
Advanced Career Outcomes
Our production systems graduates secure senior roles at Europe's most innovative tech companies
"The Kubernetes and distributed training modules were game-changers. I now architect ML platforms handling 50M+ daily recommendations. The production systems knowledge directly translated to my role."
"Real-time inference optimization and A/B testing frameworks from this program enabled me to lead SAP's ML infrastructure team. The enterprise focus was exactly what I needed."
Enterprise ML Infrastructure Stack
Master the complete technology stack powering Europe's largest ML platforms
Container Orchestration
Kubernetes, Kubeflow, Docker Swarm for ML workload management
Distributed Computing
Apache Spark, Dask, Ray for large-scale data processing
Model Serving
TensorFlow Serving, TorchServe, NVIDIA Triton for inference
Monitoring & Observability
Prometheus, Grafana, ELK Stack for ML system monitoring
Enterprise-Grade Infrastructure
Multi-Cloud Architecture
Access to AWS, GCP, and Azure ML platforms with enterprise credits for large-scale experimentation
GPU Cluster Access
Dedicated NVIDIA A100 and V100 clusters for distributed training experiments
Production Datasets
Real-world datasets from European fintech and e-commerce partners for authentic project experience
Production Security & Compliance
Enterprise-grade security protocols and compliance frameworks for production ML systems
Security & Governance
Zero Trust Architecture
Implement enterprise security patterns with role-based access control, API authentication, and encrypted data pipelines.
Model Governance
Establish model versioning, approval workflows, and audit trails meeting financial services regulatory requirements.
Infrastructure Monitoring
Comprehensive observability with automated alerting, performance monitoring, and incident response procedures.
Compliance Standards
For Experienced ML Engineers
Advanced specialization designed for professionals ready to architect enterprise ML systems
Senior ML Engineers
Experienced ML practitioners looking to advance into platform engineering, infrastructure design, and enterprise-scale system architecture roles.
Platform Engineers
DevOps and platform engineers transitioning to ML infrastructure, seeking to build and maintain scalable ML platforms for data science teams.
Technical Leaders
Engineering managers and technical leads responsible for ML strategy, team building, and making architectural decisions for enterprise ML initiatives.
Prerequisites for Success
Advanced Performance Metrics
Rigorous assessment framework measuring enterprise-level ML system design capabilities
Production-Grade Assessment
System Architecture Reviews
Design and defend ML architectures capable of handling millions of daily predictions with sub-100ms latency requirements.
Production System Build
Deploy end-to-end ML systems with monitoring, alerting, and auto-scaling on enterprise cloud infrastructure.
Technical Leadership Simulation
Lead cross-functional teams through ML platform migrations and technical decision-making processes.
Advanced Skill Progression
Professional Certifications & Recognition
AWS ML Specialty
Advanced certification in machine learning on AWS with exam preparation and practice tests
Kubernetes Certified
CKA (Certified Kubernetes Administrator) certification with focus on ML workloads
Enterprise Portfolio
Production-grade ML systems portfolio showcasing scalable architectures and real impact metrics
Complete Your ML Journey
Explore our foundational program or advance to career transformation
ML Engineering Foundations
Comprehensive introduction to ML engineering covering Python, TensorFlow/PyTorch fundamentals, and MLOps basics with hands-on projects.
Learn MoreCareer Transformation
Immersive bootcamp with guaranteed job placement, industry mentorship, and direct recruitment pipeline to top tech companies across the DACH region.
Learn MoreReady for Advanced ML Systems?
Join our next production systems cohort starting August 19, 2025. Early bird pricing ends July 30th.