AI Workloads on Kubernetes: Deploying Machine Learning Models at Scale Training Course
Kubernetes provides a scalable infrastructure for the deployment, serving, and administration of machine learning models within production settings.
This instructor-led, live training session (available online or onsite) is designed for intermediate-level professionals seeking to reliably operationalize ML models on Kubernetes for government operations.
Upon completion of this training, participants will acquire the competencies to:
- Containerize and prepare ML models for efficient and secure deployment.
- Serve models utilizing contemporary inference frameworks.
- Optimize workloads through autoscaling, GPU configuration, and resource tuning.
- Implement model rollout strategies, including A/B testing and canary deployments.
Course Format
- Integrated lecture, architectural analysis, and facilitated discussion.
- Comprehensive practical exercises reflecting real-world deployment scenarios.
- Direct implementation within a live Kubernetes environment.
Customization Options for Course Content
- If the course requires adaptation to specific environmental or toolchain requirements, please contact us to explore customization options for government stakeholders.
Course Outline
Preparation of Machine Learning Models for Operational Deployment
- Encapsulation of models using Docker
- Model export from TensorFlow and PyTorch frameworks
- Protocols for versioning and storage management
Serving Models on Kubernetes Infrastructure
- Overview of inference server capabilities
- Implementation of TensorFlow Serving and TorchServe
- Configuration of model endpoints
Techniques for Inference Optimization
- Strategies for batch processing
- Management of concurrent request handling
- Tuning for latency and throughput performance
Autoscaling Mechanisms for ML Workloads
- Horizontal Pod Autoscaler (HPA) configuration
- Vertical Pod Autoscaler (VPA) usage
- Kubernetes Event-Driven Autoscaling (KEDA) integration
GPU Provisioning and Resource Governance
- Configuration of GPU-enabled nodes
- Overview of the NVIDIA device plugin
- Defining resource requests and limits for ML workloads
Model Rollout and Release Methodologies
- Blue/green deployment strategies
- Canary rollout patterns
- A/B testing for model performance evaluation
Monitoring and Observability for Production ML Systems
- Key metrics for inference workloads
- Best practices for logging and tracing
- Dashboard creation and alerting configuration
Security and Reliability Frameworks
- Securing model endpoints for government use
- Implementation of network policies and access controls
- Ensuring high availability and resilience
Summary and Future Initiatives
Requirements
- Knowledge of containerized application workflows
- Practical experience with Python-based machine learning models
- Familiarity with core Kubernetes concepts
Target Audience
- ML engineers
- DevOps engineers
- Platform engineering teams
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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Testimonials (1)
I like the Kubernetes part of the training as I had some tasks with K8s pods and ports and it was nice to get a better understanding now. Thank you!
Laura Miron - Betfair
Course - Docker and Kubernetes
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