Technologies / Artificial Intelligence

Kubeflow on the Azure platform

The training offers an advanced approach to implementing and managing machine learning workflows using Kubeflow on the Microsoft Azure platform. The program combines a deep understanding of the Kubeflow architecture with the practical aspects of its implementation in the Azure cloud environment. The workshop is conducted as an intensive hands-on class where participants work on real ML projects, learning how to orchestrate the entire lifecycle of machine learning models, from experimentation to production deployment.

Issues

Kubeflow Architecture
Machine learning pipelines
Orchestrating experiments
Model management
Scaling up ML solutions
Monitoring and debugging
CI/CD for ML
Performance optimization
MLOps best practices
Distributed training
Model serving
Cost management

Benefits

The participant will gain advanced knowledge in implementing and managing ML workflows in a Kubeflow environment on Azure
Will develop the ability to design scalable machine learning pipelines tailored to production requirements
Will learn to effectively manage the entire lifecycle of ML models, from experimentation to deployment
Will learn performance and cost optimization techniques in the context of machine learning in the cloud
Will be able to implement MLOps best practices in their projects
Will master advanced ML pipeline monitoring and debugging techniques

Who is this training for?

ML/AI Engineers

DevOps specializing in ML

Architects of cloud solutions

Data Scientists working with production models

MLOps specialists

ML platform engineers

AI application developers

Prerequisites

Knowledge of the basics of machine learning
Experience with Kubernetes
Familiarity with the Azure platform
Python programming basics

Training program

Integration with Azure Kubernetes Service
ML components and pipelines
Configuring the environment

Pipeline design
Orchestrating experiments
Data management
Process monitoring

Automating model training
Hyperparameter tuning
Distributed training

Implementation and maintenance

Scaling up solutions
Performance monitoring
Cost 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

What are the prerequisites for this training?

For Kubeflow on the Azure platform we recommend: Knowledge of the basics of machine learning; Experience with Kubernetes; Familiarity with the Azure platform.

What is the format and duration of this training?

The training lasts 4 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: ML/AI Engineers; DevOps specializing in ML; Architects of cloud solutions.