Advanced ML Systems – Scalability, Deep Learning & MLOps

This advanced course is designed for participants who already have solid machine-learning experience and now want to learn how production-ready ML systems are built at scale.
The training dives into deep learning, scalable training workflows, data pipelines, monitoring, experiment tracking and modern MLOps practices – structured, reproducible and industry-aligned.

You will work with real-world business problems, professional development environments and build an advanced end-to-end ML system that reflects a real production scenario.

Who this course is designed for

ML practitioners with experience in regression, classification or clustering
Data analysts, developers and researchers who want to understand production-ready ML workflows
Teams wanting to build ML models that are more structured, reproducible and professionally engineered
People who want to learn how ML prototypes become scalable, deployable applications

What you will learn in this course

Develop, tune and evaluate advanced ML models
Prepare and transform complex datasets using modern methods
Detect and fix issues such as overfitting, class imbalance or data leakage
Build reproducible ML pipelines with scikit-learn
Version, test, save and provide models as APIs
Plan, implement and document a complete advanced ML project

Course Information

Duration: 5 days (extendable to 8 days with optional modules)
Format: On-site or live online
Method: 30% theory, 50% hands-on, 20% project
Tools: Python 3.9+, pandas, NumPy, scikit-learn, XGBoost, Imbalanced-Learn, Joblib
Environment: VS Code or PyCharm, Git for versioning
Group Size: Small groups for personalized guidance

Ready for the next step?

Sign up and we’ll support you in reaching your goals with confidence. Simply fill out the form — we’ll get back to you shortly with more information.