Technologies / Artificial Intelligence

TensorFlow Serving - ML Model Deployment

The training focuses on practical aspects of deploying machine learning models using TensorFlow Serving. The program is designed so that participants learn the full process of preparing and deploying ML models in a production environment. During workshops, participants go through the entire model lifecycle - from exporting from the training environment, through server configuration, to deployment and monitoring in production. Sessions are conducted in practical format, where each topic is immediately verified through implementation in a real environment.

Why choose this training?

Growing market demands mean that, technical competencies have become a critical asset. The training focuses on practical aspects of deploying machine learning models using TensorFlow Serving. The program is designed so that participants learn the full process of preparing and deploying ML models in a production environment.

Upon completion, participants will be able to: After completing the training, participants will have practical skills in deploying ML models using TensorFlow Serving, They will gain knowledge about preparing models for deployment, configuring production environment, optimizing performance, and monitoring running models, Participants will learn methods of integration with existing systems, model version management techniques, and best practices in ML operationalization. These competencies directly translate into higher effectiveness in IT projects.

This training is particularly valuable for: It is particularly valuable for ML specialists dealing with model operationalization, MLOps engineers working on ML process automation, application developers using ML models, and solution architects designing ML systems, The program is also suitable for teams implementing their first machine learning projects in production.

What makes our approach unique?

EITT combines theoretical knowledge with intensive practical workshops. Over 1 day of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but practical application skills.

With over 2,500 trainings in our portfolio and a 4.8/5 participant rating, EITT is a trusted partner in IT competency development for organizations of all sizes. Our trainers are experienced practitioners who share up-to-date knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

Deploying ML models using TensorFlow Serving
They will gain knowledge about preparing models for deployment, configuring production environment, optimizing performance, and monitoring running models
Methods of integration with existing systems, model version management techniques, and best practices in ML operationalization

Who is this training for?

It is particularly valuable for ML specialists dealing with model operationalization, MLOps engineers working on ML process automation, application developers using ML models, and solution architects designing ML systems

The program is also suitable for teams implementing their first machine learning projects in production

Prerequisites

Before starting the training, participants should have basic knowledge of machine learning and experience working with TensorFlow
Basic knowledge of Linux systems and containerization is also recommended