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Duration 14 hours
Course Outline
MLOps Foundations on Kubernetes
- Essential MLOps concepts
- Distinguishing MLOps from traditional DevOps
- Addressing key challenges in ML lifecycle management
Containerization of ML Workloads
- Packaging models alongside training code
- Optimizing container images specifically for ML tasks
- Handling dependencies to ensure reproducibility
Implementing CI/CD for Machine Learning
- Structuring ML repositories to support automation
- Incorporating testing and validation stages
- Automating pipeline triggers for retraining and updates
GitOps in Model Deployment
- Principles and workflows of GitOps
- Leveraging Argo CD for deploying models
- Managing version control for models and configurations
Orchestrating Pipelines on Kubernetes
- Constructing pipelines using Tekton
- Overseeing multi-step ML workflows
- Handling scheduling and resource allocation
Monitoring, Logging, and Rollback Tactics
- Monitoring data drift and model performance
- Integrating alerting systems and observability tools
- Implementing rollback and failover procedures
Automated Retraining and Continuous Enhancement
- Establishing effective feedback loops
- Automating scheduled retraining cycles
- Utilizing MLflow for tracking and managing experiments
Advanced MLOps Architectures
- Multi-cluster and hybrid-cloud deployment strategies
- Enabling team scaling via shared infrastructure
- Addressing security and compliance requirements
Recap and Future Directions
Requirements
- A solid grasp of Kubernetes fundamentals
- Practical experience with machine learning workflows
- Familiarity with Git-based development practices
Target Audience
- ML engineers
- DevOps engineers
- ML platform teams
Testimonials (3)
About the microservices and how to maintenance kubernetes
Yufri Isnaini Rochmat Maulana - Bank Indonesia
Course - Advanced Platform Engineering: Scaling with Microservices and Kubernetes
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
The knowledge and the patience from the trainer to answer to our questions.