Technologies / Artificial Intelligence

Kubeflow on OpenShift - ML orchestration on Kubernetes

The training presents an advanced approach to implementing machine learning solutions on the OpenShift platform using Kubeflow. The program is carefully designed to guide participants through the process of building scalable ML systems in a Kubernetes environment. During the intensive workshop, participants will learn the full lifecycle of ML applications - from preparing the environment, to implementing ML pipelines, to deploying and monitoring in production. The class combines a solid theoretical foundation with hands-on experience, enabling an understanding of both the architectural and operational aspects of the platform.

Issues

The training program covers a wide range of topics related to implementing ML on the Kubernetes platform. Participants will learn about Kubeflow architecture, managing Jupyter environments, automating ML pipelines, distributed training, serving models, monitoring and analytics, and operational best practices. Special emphasis is placed on the practical aspects of building scalable ML systems in a production environment.

Benefits

Implementing and managing ML solutions on the OpenShift platform using Kubeflow
They will gain a working knowledge of system architecture, automation of ML processes, management of computing resources and monitoring of the production environment
Methods to effectively utilize Kubernetes infrastructure for ML tasks, performance optimization techniques and best practices in operationalizing AI systems

Who is this training for?

It is particularly valuable for platform engineers responsible for ML infrastructure, machine learning specialists working on scalable solutions, OpenShift administrators interested in ML deployments, and solution architects designing AI systems in the cloud

The program is also suitable for teams transitioning to the Kubeflow platform in an OpenShift environment

Prerequisites

Prior to training, participants should have a working knowledge of Kubernetes and OpenShift, and basic experience with machine learning models
Familiarity with CI/CD processes and DevOps practices is also advisable

Training program

Installation and configuration of the platform
Management of users and permissions

Jupyter on Kubeflow

Integration with storage systems

Automation of development environments
ML streams and experiments

Experiment management
Tracking metrics and artifacts

Training and implementation
Distributed training on Kubernetes
Model serving with KFServing
Monitoring and observability
Cluster performance optimization

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

Who is the Kubeflow on OpenShift - ML orchestration on Kubernetes training for?

This training is designed for professionals looking to develop skills in kubeflow on openshift - ml orchestration on kubernetes. Required level: intermediate.

How long is the Kubeflow on OpenShift - ML orchestration on Kubernetes training?

The training lasts 4. Available in online or on-site format.

Will I receive a certificate?

Yes — every participant receives a completion certificate confirming acquired competencies. EITT holds ISO 9001 accreditation.

Can this training be conducted for a closed group?

Yes — we offer dedicated closed trainings for companies. We customize the program to your team's needs. Contact us for an individual quote.