AI/ML Deployment on Kubernetes for Advanced Cloud Engineers

Master the deployment, scaling, and management of AI/ML workloads on Kubernetes, from containerization to automated pipelines and monitoring.
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Containerizing and Deploying AI/ML Applications on Kubernetes

Unit 1: Dockerizing AI/ML Applications

Unit 2: Deploying to Kubernetes

Unit 3: Basic K8s Resource Management

Managing GPU Resources and Model Serving in Kubernetes

Unit 1: GPU Management in Kubernetes

Unit 2: Model Serving with Kubernetes

Unit 3: Optimizing Resource Utilization

Automating ML Pipelines with Kubeflow

Unit 1: Introduction to Kubeflow

Unit 2: Building ML Pipelines with Kubeflow Pipelines

Unit 3: Advanced Pipeline Techniques

Unit 4: Model Serving with KFServing

Monitoring, Scaling, and Securing AI/ML Deployments

Unit 1: Monitoring AI/ML Deployments with Prometheus and Grafana

Unit 2: Scaling AI/ML Deployments on Kubernetes

Unit 3: Securing AI/ML Workloads on Kubernetes

Unit 4: Troubleshooting AI/ML Deployments