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
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Kubeflow Architecture
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Machine learning pipelines
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Orchestrating experiments
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Model management
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Scaling up ML solutions
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Monitoring and debugging
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CI/CD for ML
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Performance optimization
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MLOps best practices
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Distributed training
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Model serving
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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?
Prerequisites
- Knowledge of the basics of machine learning
- Experience with Kubernetes
- Familiarity with the Azure platform
- Python programming basics
Training program
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
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.
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