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Duration 21 hours
Course Outline
Overview of Operational Integration Strategies
- Primary obstacles associated with the operational implementation of specialized AI models
- Distinctions between development stages and live operational environments
- Established frameworks and technologies for model integration
Readiness Assessment for Operational Release
- Converting models to interoperable standards (e.g., ONNX, TensorFlow SavedModel)
- Refining model architecture to minimize response times and maximize processing capacity
- Validating model performance against anomalous scenarios and empirical datasets
Containerization Standards for Model Operations
- Fundamentals of Docker infrastructure
- Construction of isolated Docker environments for machine learning assets
- Protocol adherence for container integrity and resource optimization
Orchestration and Scaling via Kubernetes
- Application of Kubernetes to artificial intelligence workloads
- Configuration of Kubernetes clusters for model hosting infrastructure
- Distribution of request loads and dynamic resource adjustment
Operational Oversight and Lifecycle Management
- Establishing observability stacks using Prometheus and Grafana
- Automated capture of system events for fault detection and performance analysis
- Continuous retraining mechanisms to mitigate model degradation and incorporate updates
Security Protocols for Live Environments
- Harden network interfaces for model inference services
- Enforcement of identity verification and access control frameworks
- Mitigation of risks related to data confidentiality and privacy compliance
Practical Applications and Laboratory Exercises
- Deployment of a sentiment analysis system
- Expansion of a machine translation service
- Establishment of monitoring controls for image classification systems
Concluding Remarks and Future Roadmap
Requirements
- Comprehensive proficiency in machine learning workflows
- Practical experience in the fine-tuning of machine learning models
- Working knowledge of DevOps or MLOps methodologies
Intended Audience
- DevOps engineers
- MLOps practitioners
- Specialists in AI deployment
Testimonials (2)
Craig was extremely involved in the training, always making sure we are paying attention, adapted the examples to our day-to-day activities and always provided an answer when asked, even if the information was not added in the presentation.
Ecaterina Ioana Nicoale - BOOKING HOLDINGS ROMANIA SRL
Course - DevOps Foundation®
High level of commitment and knowledge of the trainer
