MLOps: lifecycle management of AI models
Training on implementing MLOps practices to automate the processes of building, testing, deploying and monitoring AI models in manufacturing environments. The program focuses on production-ready ML pipelines and operational excellence. The workshop includes hands-on implementation of MLOps tools and best practices. DevOps-inspired methodologies ensure reliable and scalable management of AI systems.
Issues
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MLOps streams
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CI/CD for ML
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Model versioning
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Automatic testing
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Implementation in production
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Monitoring models
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Drift detection
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Infrastructure automation
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Containerization
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Scaling strategies
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Performance optimization
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Responding to incidents
Benefits
- Automating machine learning pipelines will reduce time to production for AI models by 60%
- Reliable deployment processes will ensure consistent model performance in production environments
- Monitoring and alerting systems will enable proactive maintenance and performance optimization
- Version control and reproducibility practices will enhance model management and compliance
- Scalable infrastructure design will provide support for growing AI workloads
- Incident response capabilities will minimize downtime and business impact
- Implementation of best practices will ensure operational excellence in AI systems management
Who is this training for?
Prerequisites
- Experience in developing machine learning models and their implementation
- Familiarity with DevOps practices and CI/CD pipelines
- Ability to work with cloud platforms and containerization technologies
- Basic knowledge of infrastructure monitoring and automation systems
Training program
MLOps basics and architecture
- ML lifecycle management
- CI/CD for machine learning
- Strategies for model versioning
- Infrastructure as code for ML
automatic learning and model testing
- Automatic data validation
- Model learning pipelines
- A/B testing framework
- Performance monitoring
implement and serve in production
- Model serving architectures
- Containerization strategies
- Load balancing and scaling
- Blue-green implementations
Monitoring and maintenance
- Detection of model drift
- Performance degradation alerts
- Automatic re-training triggers
- Incident response procedures
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 MLOps: lifecycle management of AI models we recommend: Experience in developing machine learning models and their implementation; Familiarity with DevOps practices and CI/CD pipelines; Ability to work with cloud platforms and containerization technologies.
What is the format and duration of this training?
The training lasts 3 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 Engineers; DevOps teams; ML platform engineers.
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Funding Options
Check funding options for your company
Development Services Database
Up to 80% funding for SMEs from EU funds
Check availabilityNational Training Fund
Up to 100% funding for employers
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We train teams at Poland's largest companies
Interested in this training?
Contact us - we'll prepare an offer tailored to your organization's needs.