Training Kubeflow on the Azure platform
Practical information about training
- CATEGORY: Technologies
- SUBCATEGORY: AI
- TRAINING CODE: IT-AI-180
- DURATION: 4 days
- PRICE INFORMATION from: 5050 PLN net
- LANGUAGE OF TRAINING: polish
- FORM OF IMPLEMENTATION: stationary, online
Training description
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.
Participant profile
- 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
Agenda
- Kubeflow and Azure Foundations
- Kubeflow Architecture
- Integration with Azure Kubernetes Service
- ML components and pipelines
- Configuring the environment
- ML workflow management
- Pipeline design
- Orchestrating experiments
- Data management
- Process monitoring
- Advanced features
- Automating model training
- Hyperparameter tuning
- Distributed training
- Model serving
- Implementation and maintenance
- CI/CD for ML
- Scaling up solutions
- Performance monitoring
- Cost optimization
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.
Required preparation of participants
- Knowledge of the basics of machine learning
- Experience with Kubernetes
- Familiarity with the Azure platform
- Python programming basics
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
Do you have any questions?
Feel free to contact us.
Anna Polak
+48 600 010 440
anna.polak@eitt.pl
31 Ząbkowska Street 03-736 Warsaw
Forms of training delivery
Stationary training
- Training at the customer's premises or at a designated location
- Training room equipped with the necessary equipment
- Training materials in electronic form
- Coffee breaks and lunch
- Direct interaction with the trainer
- Networking in a group
- Workshop exercises in teams
Remote training
- Virtual training environment
- Electronic materials
- Interactive online exercises
- Breakout rooms for group work
- Technical support during the training
- Recordings of the session (optional)
Possibility of funding
The training can be financed with public funds under:
- National Training Fund (KFS)
- Development Services Base (BUR)
- EU projects implemented by PARP
- HR Academy Program (PARP)
- Regional operational programs
If you are interested in funding, our team will help you prepare the required documentation.
HAVE A QUESTION?
Contact us for more information about our training, programs and cooperation. We will be happy to answer all your inquiries!
They trusted us
Get to know our company

Do you have any questions?
Feel free to contact us.
Anna Polak
+48 600 010 440
anna.polak@eitt.pl
31 Ząbkowska Street 03-736 Warsaw
FAQ - Frequently Asked Questions
- One-pager invitation with deadlines
- Project kick-off
- Strategic leadership and thinking
- Communication and Cooperation. Conflict management
- Motivating, engaging and difficult decisions in business
- Managing Change and Innovation. Leadership in crisis
- Building the organization of the future
- Best practices workshop - retrospective; creating a coherent program for middle and lower management levels























