Training MLOps for Azure Machine Learning – automation and management
Practical information about training
- CATEGORY: Technologies
- SUBCATEGORY: AI
- TRAINING CODE: IT-AI-117
- DURATION: 2 days
- PRICE INFORMATION from: 2450 PLN net
- LANGUAGE OF TRAINING: polish
- FORM OF IMPLEMENTATION: stationary, online
Training description
The training focuses on the practical aspects of implementing MLOps practices in projects using Azure Machine Learning. The program covers ML process automation, model lifecycle management and integration with DevOps tools. Participants learn to design and implement scalable MLOps solutions to ensure repeatable and reliable ML processes.
Participant profile
- DevOps Engineers working on ML projects
- Data Scientists interested in ML automation
- ML Engineers responsible for implementations
- AI/ML solution architects
- ML platform engineers
- ML process automation specialists
- Team Leaders of ML teams
Agenda
- MLOps basics
- MLOps philosophy and practices
- Integration with Azure DevOps
- Management of environments
- Process automation
- ML pipeline automation
- Design workflows
- Continuous Training (CT)
- Continuous Deployment (CD)
- Monitoring and alerts
- Model management
- Versioning of models and data
- Model register
- Tracking experiments
- Validation of models
- Infrastructure and scalability
- Resource management
- Cost optimization
- Safety and compliance
- Scalability of solutions
Benefits
The participant will develop advanced skills in implementing MLOps practices in an Azure Machine Learning environment. Will gain a working knowledge of ML process automation and integration with DevOps tools. Will learn to design and implement scalable solutions to ensure repeatable ML processes. Will develop the ability to effectively manage the lifecycle of machine learning models. Will learn techniques for cost optimization and resource management in ML projects. Will gain the ability to build reliable and secure ML pipelines.
Required preparation of participants
- Experience with Azure Machine Learning
- Knowledge of the basics of DevOps
- Practical knowledge of machine learning
- Python programming basics
Issues
- MLOps practices
- ML automation
- CI/CD Pipelines
- Version management
- Monitoring of models
- ML infrastructure
- Cost optimization
- MLOps security
- Scalability of solutions
- Management of environments
- DevOps best practices
- Tracking experiments
Do you have any questions?
Feel free to contact us.
Monika Fengler
+48 532 081 700
monika.fengler@eitt.academy
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!
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Do you have any questions?
Feel free to contact us.
Monika Fengler
+48 532 081 700
monika.fengler@eitt.academy
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























