Advanced Machine Learning – Scaling, Deep Learning & MLOps

This course is designed for participants with solid ML knowledge who want to take the next step into scalable, modern machine-learning systems.
The focus is on deep learning, modern model architectures, high-performance training pipelines, professional MLOps processes, and production-ready deployments.

You will learn how to develop complex models for image and text data, train them at scale, and deliver them in production environments – from experiment tracking to orchestration and monitoring.
The final component is a challenging end-to-end project that integrates multiple components into a fully functional ML system.

Who this course is designed for

Data scientists, ML engineers and experienced developers
Individuals who already train, tune and evaluate ML models
Teams that want to scale ML systems or make them production-ready
Practitioners who want to understand deep learning, MLOps and modern infrastructure

What you will learn in this course

Develop, train and optimize deep-learning models for images and text
Scale ML systems for large datasets, fast inference and production workflows
Apply MLOps tools such as MLflow, Prefect/Airflow and DVC
Implement monitoring, drift detection and interpretability mechanisms
Serve models via FastAPI & Docker and operate them in a controlled environment
Plan, build and deploy a challenging end-to-end ML project

Course Information

Duration: 5 days (extendable to 8 days for deeper insights and project scaling)
Format: On-site or live online
Methodology: 40% theory, 40% hands-on, 20% project
Tools: PyTorch, TensorFlow/Keras, scikit-learn, XGBoost, MLflow, Prefect, DVC, Docker
Infrastructure: VS Code / PyCharm, Git, cloud or local environments
Group size: Small groups for individual feedback

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.