Master the art of deploying, managing, and scaling AI/ML workloads, including Large Language Models, directly on Kubernetes, leveraging your SRE expertise for robust and efficient MLOps.
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Foundations of MLOps on Kubernetes
Unit 1: MLOps: The SRE Perspective
Unit 2: K8s Primitives for ML
Unit 3: Orchestrating ML Workloads
Unit 4: Storage & Networking for ML
Unit 5: Challenges & Best Practices
Kubernetes Primitives for ML Workloads
Unit 1: Batch ML with K8s Jobs
Unit 2: Serving ML Models with K8s
Unit 3: Persistent Storage for ML
Data Management Strategies for ML on K8s
Unit 1: Kubernetes Storage Essentials for ML
Unit 2: Kubernetes Storage Solutions for ML
Unit 3: Efficient Data Access for ML Workloads
Unit 4: Managing Large Datasets & Versioning
Unit 5: Advanced Data Management & Governance
Distributed ML Training Orchestration
Unit 1: Distributed Training Fundamentals
Unit 2: Frameworks for Distributed ML
Unit 3: Kubernetes for Distributed ML
Managing ML Training with Kubernetes Jobs
Unit 1: Kubernetes Jobs for ML Training
Unit 2: Robustness and Retries for ML Jobs
Unit 3: Orchestrating ML Workflows with Jobs
Accelerating Training with Kubeflow Training Operators
Unit 1: Introduction to Kubeflow Training Operators
Unit 2: Advanced Operator Configuration
Unit 3: Troubleshooting and Advanced Topics
GPU and Specialized Hardware Management
Unit 1: GPU Fundamentals for Kubernetes
Unit 2: Kubernetes GPU Scheduling & Allocation
Unit 3: Advanced Hardware Management & Optimization
Scalable Model Serving Architectures on Kubernetes
Unit 1: Model Serving Fundamentals
Unit 2: Designing Resilient Serving
Unit 3: Serving Trade-offs & Costs
Implementing Model Serving with KServe
Unit 1: KServe Fundamentals
Unit 2: Frameworks & Configurations
Unit 3: Autoscaling & Traffic
Advanced Model Serving Strategies
Unit 1: Beyond Basic Serving
Unit 2: Autoscaling Your Models
Unit 3: Advanced Traffic Control
Building End-to-End MLOps Pipelines with Kubeflow Pipelines
Unit 1: Kubeflow Pipelines Fundamentals
Unit 2: Building Complex MLOps Workflows
Unit 3: Monitoring and Managing KFP
Automating MLOps with Argo Workflows
Unit 1: Argo Workflows Fundamentals for MLOps
Unit 2: Translating MLOps Stages to Argo
Unit 3: Advanced Argo MLOps Integration
Comprehensive Observability for ML Workloads
Unit 1: Foundations of ML Observability
Unit 2: Model Performance Monitoring
Unit 3: Data Quality & Drift Detection
Operationalizing Large Language Models (LLOps) on Kubernetes
