Data Science Engineering Track
20-tygodniowy program zawodowy łączący data engineering i science. Platformy chmurowe, MLOps, deep learning i wdrażanie modeli w środowisku produkcyjnym.
Najwyższy poziom expertise
Kompleksowy program dla doświadczonych data scientists, którzy chcą zostać engineering leaders i budować systemy ML w enterprise scale.
MLOps i deployment
CI/CD dla modeli ML, containerization, monitoring produkcyjnych systemów AI i automated retraining.
Cloud architecture
AWS/Azure dla data science, serverless ML, distributed computing i cost optimization w cloud.
Deep learning production
TensorFlow/PyTorch w skali, model optimization, edge deployment i real-time inference.
Filozofia full-stack data scientist
Program oparty na filozofii T-shaped professionals - głęboka ekspertyza w ML/AI z szeroką wiedzą o całym lifecycle produktów data-driven. Uczysz się myśleć jak tech lead, który rozumie zarówno business impact jak i technical constraints.
- End-to-end ownership produktów ML
- Cross-functional collaboration z engineers
- Technical leadership i mentoring
- Scalable systems design
- Business value optimization
- Portfolio ready for FAANG
20-tygodniowa transformacja
Intensywny program podzielony na 5 modułów - od data engineering fundamentals do production-grade ML systems
Data Engineering Fundamentals (Tygodnie 1-4)
Budowanie solidnych fundamentów w data engineering. ETL/ELT pipelines, data modeling, distributed systems i infrastructure as code. Przejście od data scientist do data engineering mindset.
Pipeline Engineering
- • Apache Airflow orchestration
- • Data quality monitoring
- • Error handling i recovery
- • Pipeline testing strategies
Cloud Infrastructure
- • Terraform i Infrastructure as Code
- • AWS/Azure data services
- • Containerization z Docker
- • Kubernetes basics
Data Architecture
- • Data lake vs warehouse design
- • Real-time vs batch processing
- • Data governance i security
- • Cost optimization strategies
MLOps i Model Lifecycle (Tygodnie 5-8)
Profesjonalne zarządzanie cyklem życia modeli ML. Od experimentów w notebooks do produkcyjnych systemów z CI/CD, monitoring i automated retraining.
Experiment Tracking
- • MLflow i experiment management
- • Model versioning strategies
- • Hyperparameter optimization
- • A/B testing dla ML
CI/CD for ML
- • GitHub Actions i GitLab CI
- • Automated testing ML pipelines
- • Model validation frameworks
- • Deployment automation
Production Monitoring
- • Model drift detection
- • Performance monitoring
- • Alerting i incident response
- • Business metrics tracking
Deep Learning w Production (Tygodnie 9-12)
Zaawansowane architektury deep learning, optimization dla production, distributed training i deployment na edge devices. TensorFlow/PyTorch w enterprise scale.
Advanced DL
- • Transformers i attention mechanisms
- • Computer vision w production
- • NLP i language models
- • Reinforcement learning basics
Model Optimization
- • Model compression techniques
- • Quantization i pruning
- • ONNX i model conversion
- • GPU optimization
Scalable Training
- • Distributed training strategies
- • Multi-GPU i multi-node
- • Mixed precision training
- • Cloud training platforms
Cloud-Native ML Systems (Tygodnie 13-16)
Serverless ML, microservices architectures, real-time inference systems i cost optimization. Budowanie systemów ML które skalują się automatycznie z traffic.
Serverless ML
- • AWS Lambda i Azure Functions
- • API Gateway i authentication
- • Cold start optimization
- • Event-driven architectures
Real-time Systems
- • Streaming ML z Kafka
- • Low-latency inference
- • Edge deployment strategies
- • WebRTC i real-time APIs
Cost & Security
- • Resource optimization
- • Auto-scaling strategies
- • Security best practices
- • Compliance i audit trails
Capstone Project i Career Transition (Tygodnie 17-20)
Realizacja end-to-end projektu z partnerską firmą. Od business problem definition do production deployment. Intensive career coaching i przygotowanie do senior roles.
Real Company Project
- • Partnership z tech companies
- • Real business problem solving
- • Stakeholder management
- • Production deployment
Leadership Skills
- • Technical leadership
- • Cross-functional collaboration
- • Project management
- • Mentoring junior members
Career Acceleration
- • Senior role preparation
- • Interview coaching
- • Salary negotiation
- • Industry networking
Rezultaty expert-level
Kompleksowa transformacja z data scientist na technical leader gotowego do najwyższych stanowisk w branży
Trajectory do tech leadership
Twoje ekspertyza po programie
Multi-cloud ML platform
Pełna architektura MLOps na AWS i Azure z automated deployment, monitoring i cost optimization
Real-time computer vision system
Production-grade CV pipeline z edge deployment, <50ms latency i automatic scaling
NLP recommendation engine
Distributed training, A/B testing framework i business impact measurement dla enterprise client
Capstone: Company partnership
Kompleksowy projekt z tech company: od problem definition do deployment w production
Dla expert-level professionals
Ten program jest dla doświadczonych data scientists gotowych na technical leadership i budowanie systemów ML w enterprise scale
Target profile
Senior Data Scientists
3+ lata commercial experience, portfolio ML projects, chcą przejść do ML engineering leadership
ML Engineers seeking advancement
Doświadczenie z production ML, chcą architectural expertise i tech leadership skills
Tech Leaders pivoting to AI
Software architects, tech leads z strong engineering background wchodzący w AI/ML space
Career objectives
FAANG/Big Tech transition
Principal/Staff Data Scientist roles w Google, Meta, Amazon, Microsoft, Apple
ML Engineering leadership
Head of ML, VP of Data Science, Chief AI Officer w tech companies i enterprises
AI startup founding
Technical co-founder skills: full-stack ML, product development, technical strategy
Prerequisites
Strong ML background
Production ML models, advanced Python/R, deep learning frameworks, statistical modeling
Software engineering skills
Git workflow, testing, code review, basic understanding of system architecture
Time commitment
20+ godzin tygodniowo, włączając weekendy. Program bardzo intensywny.
Admission process
Program ma bardzo wysokie wymagania wstępne. Acceptance rate ~30%. Portfolio review, technical interview i problem-solving assessment. Oczekujemy proven track record w ML i gotowość do intense learning.
Cutting-edge tech stack
Najnowsze technologie używane w FAANG companies i top AI labs. Portfolio które imponuje tech recruiters na całym świecie.
Production-grade stack
Cloud Native
AWS/Azure/GCP, Kubernetes
MLOps Tools
MLflow, Kubeflow, Ray
Deep Learning
PyTorch, TensorFlow 2.0
Data Engineering
Spark, Kafka, Airflow
Advanced tech ecosystem
Leadership metodologia
Systems Thinking
End-to-end ownership: od business problem do production monitoring. Understanding całej architektury.
Technical Leadership
Mentoring, architectural decisions, cross-team collaboration. Skills beyond pure technical expertise.
Production Excellence
SLA/SLI, incident response, scalability planning. Enterprise-grade operational mindset.
Business Impact Focus
ROI measurement, stakeholder communication, strategic tech decisions aligned z business goals.
Unique program features
- Real company partnerships for capstone
- Industry mentors z FAANG background
- Private cloud infrastructure dla praktyki
- Alumni network w top tech companies
Exclusive program access
Limited cohort size (max 8 uczestników) i rigorous admission process. Premium investment w najwyższej klasy training.
Expert Track
- 20 tygodni expert-level programu
- Cloud infrastructure access (AWS/Azure)
- FAANG mentors (8 sesji 1-na-1)
- Capstone z real company
- 24 miesiące alumni support
- Career transition coaching
Enterprise
- Team training (4-8 senior data scientists)
- Customizacja do tech stack firmy
- Capstone projects na company data
- Architectural consulting
- Ongoing technical support
- ROI measurement i KPIs
Rigorous admission process
Application
Detailed background, portfolio link, career objectives, motivation letter
Portfolio Review
Deep technical review Twojego GitHub, production projects, code quality
Technical Interview
60-min system design, ML architecture, coding challenge z senior engineer
Final Decision
Admission committee review, notification w 48h, enrollment process
Onboarding
Infrastructure setup, mentor assignment, pre-program materials (2 tygodnie)
Inne ścieżki rozwoju
Sprawdź programy dla innych poziomów zaawansowania
Data Science Starter Pack
8-tygodniowe wprowadzenie do Python, pandas, NumPy i podstaw machine learning. Pierwszy krok w data science dla początkujących.
Advanced Analytics Program
14-tygodniowe kompleksowe szkolenie z modelowania statystycznego, R programming, SQL i enterprise analytics tools.
Dołącz do tech elite
Data Science Engineering Track to twoja droga do tech leadership w najlepszych firmach świata. Elite program dla ambitnych professionals.
🔥 Następna kohorta startuje 1 września 2025 • Pozostało tylko 3 miejsca (max 8 uczestników)