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Εξέλιξη Κομματιού
Introduction to AI-Enhanced Kubernetes Operations
- Why AI matters for modern cluster operations
- Limitations of traditional scaling and scheduling logic
- Key concepts of ML for resource management
Foundations of Kubernetes Resource Management
- CPU, GPU, and memory allocation fundamentals
- Understanding quotas, limits, and requests
- Identifying bottlenecks and inefficiencies
Machine Learning Approaches for Scheduling
- Supervised and unsupervised models for workload placement
- Predictive algorithms for resource demand
- Using ML features in custom schedulers
Reinforcement Learning for Intelligent Autoscaling
- How RL agents learn from cluster behavior
- Designing reward functions for efficiency
- Building RL-driven autoscaling strategies
Predictive Autoscaling with Metrics and Telemetry
- Using Prometheus data for forecasting
- Applying time-series models to autoscaling
- Evaluating prediction accuracy and tuning models
Implementing AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Deploying intelligent control loops
- Extending KEDA for AI-assisted decision-making
Cost and Performance Optimization Strategies
- Reducing compute costs through predictive scaling
- Improving GPU utilization with ML-driven placement
- Balancing latency, throughput, and efficiency
Practical Scenarios and Real-World Use Cases
- Autoscaling high-load applications with AI
- Optimizing heterogeneous node pools
- Applying ML to multi-tenant environments
Summary and Next Steps
Απαιτήσεις
- An understanding of Kubernetes fundamentals
- Experience with containerized application deployments
- Familiarity with cluster operations and resource management
Audience
- SREs working with large-scale distributed systems
- Kubernetes operators managing high-demand workloads
- Platform engineers optimizing compute infrastructure
21 Ώρες
Σχόλια (5)
There was a lot to lean, but it never felt rushed.
thomas gardner - National Oceanography Centre
Κομμάτι - Docker, Kubernetes and OpenShift for Administrators
Μηχανική Μετάφραση
It is an in-deep Kubernetes training covering all important aspects to manage Kubernetes, be it in the cloud or on-premise, but the pace is gradual and well adjusted, so the training can be followed very well by students who have had no prior exposure to Kubernetes, as it builds up knowledge from the ground up.
Volker Kerkhoff
Κομμάτι - Docker and Kubernetes: Building and Scaling a Containerized Application
Μηχανική Μετάφραση
提供了对Docker和Kubernetes的良好基础。 (Note: The numbers Docker and Kubernetes are kept as is, assuming they refer to specific course codes or identifiers that should not be translated.) However, following the guideline of returning original if untranslatable while preserving meaning: It gave a good grounding for Docker and Kubernetes.
Stephen Dowdeswell - Global Knowledge Networks UK
Κομμάτι - Docker (introducing Kubernetes)
Μηχανική Μετάφραση
I generally liked the trainer knowledge and enthusiasm.
Ruben Ortega
Κομμάτι - Docker and Kubernetes
Μηχανική Μετάφραση
Από κατά πλειονή τρέχουσα τη γνώση του εκπαιδευτή. (Note: The sentence structure has been adjusted to sound more natural in Greek while preserving the original meaning.)
- Inverso Gesellschaft fur innovative Versicherungssoftware mbH
Κομμάτι - Docker, Kubernetes and OpenShift for Developers
Μηχανική Μετάφραση
