Technologies / Artificial Intelligence

Kubeflow - machine learning orchestration on Kubernetes

Intensive training focusing on using the Kubeflow platform to orchestrate machine learning processes in a Kubernetes environment. The program guides participants through the process of building, deploying and managing ML pipelines in a distributed environment, combining theory with intensive hands-on practice. Hands-on workshops make up 70% of the training time, where participants work on real use cases, building end-to-end ML solutions using DevOps and MLOps best practices. Each topic is discussed in the context of practical production challenges, including aspects of scalability, reliability and efficiency.

Issues

Kubeflow Architecture
ML pipelines
Kubernetes for ML
Model serving
Experiment tracking
Resource management
Pipeline optimization
Version control
Model deployment
Monitoring of ML systems
Security practices
Infrastructure automation

Benefits

Upon completion of the training, the participant will have a comprehensive knowledge of designing and implementing ML solutions in the Kubernetes environment using Kubeflow
He or she will gain practical skills in building scalable ML pipelines that can support complex learning and inference processes
Will develop competence in managing the entire lifecycle of ML models in a production environment
Will learn to efficiently utilize computational resources and optimize the performance of ML pipelines
Will be able to implement MLOps best practices, ensuring reliable and secure implementations
Will gain the ability to integrate ML solutions with existing DevOps infrastructure and CI/CD processes

Who is this training for?

MLOps engineers implementing solutions in production

DevOps specialists working with ML systems

Cloud solution architects for AI/ML

Data scientists interested in the operational aspects of ML

Platform engineers dealing with ML infrastructure

ML systems implementation specialists

Kubernetes cluster administrators

Teams responsible for the continuous integration and implementation of ML

Prerequisites

Practical knowledge of Kubernetes and containerization
Experience in the implementation of ML solutions
Knowledge of the basics of DevOps and CI/CD
Python programming skills

Training program

Integration with Kubernetes
Cluster resource management
Setting up the development environment
Pipelines and orchestration

Kubeflow components and operators

Monitoring and debugging streams
Advanced implementations

Model version management
Performance optimization

MLOps and DevOps practices
Continuous integration for ML

Production monitoring
Security and access control

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

What are the prerequisites for this training?

For Kubeflow - machine learning orchestration on Kubernetes we recommend: Practical knowledge of Kubernetes and containerization; Experience in the implementation of ML solutions; Knowledge of the basics of DevOps and CI/CD.

What is the format and duration of this training?

The training lasts 5 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: MLOps engineers implementing solutions in production; DevOps specialists working with ML systems; Cloud solution architects for AI/ML.