Machine Learning Engineer
Develop competencies in building, training and deploying machine learning models. Master ML platforms (Azure ML, SageMaker), deep learning, computer vision and AutoML.
Featured Trainings
Azure Machine Learning - from basics to advanced techniques
The training provides an in-depth understanding of the Azure Machine Learning platform, from fundamental concepts to advanced implementation techniques. The program combines the theoretical foundations of machine learning with hands-on use of Azure ML tools. Participants go through the full ML project lifecycle, from data preparation to model implementation, learning best practices and design patterns.
View trainingComputer Vision with Python
Specialized training on computer vision techniques using Python language and modern CV libraries. The workshop program covers both classical image processing methods and advanced techniques based on deep learning. Participants will learn practical applications of computer vision in a variety of fields, from simple image analysis to complex object recognition and motion tracking systems.
View trainingAmazon SageMaker - ML platform
The training focuses on the practical use of the Amazon SageMaker platform in machine learning projects. Participants will learn the full lifecycle of an ML model - from data preparation, to training, to production deployment. The program combines workshop sessions with theoretical lectures, providing a balanced approach to learning. Classes are conducted in an interactive format, with an emphasis on practical exercises on real use cases.
View trainingMachine Learning Engineer Path
This path prepares you for the ML Engineer role — from cloud platforms (Azure ML, SageMaker), through AutoML frameworks and deep learning, to specializations in computer vision and video analytics. The program covers both mathematical theory and practical model deployment to production.
Path 1: Goal
Cloud ML Platforms — Azure Machine Learning and Amazon SageMaker.
Recommended EITT Trainings
Rationale
Azure ML and SageMaker are leading MLOps platforms in the cloud. They enable the full model lifecycle — from experimentation to deployment and monitoring. Spark MLlib adds large-scale processing capabilities.
Path 2: Goal
Advanced ML techniques — computer vision, deep learning and AI data analytics.
Recommended EITT Trainings
Rationale
Computer vision and advanced ML techniques are the fastest growing AI areas. Python trainings provide a solid practical foundation, and intensive workshops ensure experience on real projects.
Path 3: Goal
ML in business practice — applications in finance, banking and data science.
Recommended EITT Trainings
Rationale
Finance and banking are among the largest ML consumers. Specialized trainings provide knowledge of specific applications — scoring, fraud detection and risk analysis.
Interested in this path?
Contact us to discuss the details of the training program and tailor it to your needs.