Online MLOps Training in Srbija

This instructor-led, live training in Srbija (online or onsite) targets advanced-level AI engineers and data scientists with intermediate-to-advanced experience who aim to enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using modern MLOps practices.

Upon completion of this training, participants will be able to:

• Optimize DeepSeek models to improve efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models across both cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

Ova obuka uživo sa instruktora u Srbija (online ili uživo na lokaciji) namenjena je developerima i naučnicima za podatke koji žele da grade, uspostave i upravljaju tokom rada mašinskog učenja na Kubernetes platformi.

Koncem ove obuke, učesnici će biti u stanju da:

• Instaliraju i konfigurisu Kubeflow u lokalnom okruženju (on-premise) i u oblaku.
• Grade, uspostave i upravljaju tokom rada ML zasnivaći se na Docker kontejnerima i Kubernetes platformi.
• Izvrše kompletne pipelajnove mašinskog učenja na različitim arhitekturama i облачним okruženjima.
• Iskoriste Kubeflow za pokretanje i upravljanje Jupyter notesbookovima.
• Grade opterećenja za obuku ML, podešavanje hiperparametara i serviranje preko više platformi.

This instructor-led, live training in Srbija (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

By the end of this training, participants will be able to:

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.