MLOps with Kubernetes Training is an advanced three-day program designed to help professionals operationalize, scale, and manage machine learning workloads using Kubernetes. This course focuses on deploying ML models as containerized services, orchestrating training and inference pipelines, managing scalability and reliability, and applying governance and security practices in Kubernetes-based environments, enabling organizations to run production-grade ML systems at scale.

• Understand MLOps with Kubernetes concepts
• Learn containerized ML deployment workflows
• Understand Kubernetes orchestration basics
• Gain knowledge of scalable ML infrastructure
• Learn CI/CD for ML concepts
• Understand model monitoring and automation
• Explore cloud-native ML operations
• Identify enterprise MLOps use case

• Machine learning engineers and ML platform engineers
• MLOps and DevOps professionals
• Cloud and Kubernetes engineers supporting ML systems
• Data scientists moving models to production
• Technology professionals managing scalable ML infrastructure

• Basic understanding of machine learning or MLOps concepts
• Familiarity with containerization fundamentals
• Awareness of cloud or distributed systems concepts
• Experience with DevOps or infrastructure workflows is beneficial

• Deploying ML models on Kubernetes clusters
• Orchestrating ML training and inference workloads
• Scaling and managing ML services reliably
• Monitoring, governing, and securing ML workloads
• Implementing production-ready MLOps pipelines with Kubernetes

By the end of this training, participants will be able to deploy, scale, monitor, and govern machine learning models using Kubernetes, design robust MLOps pipelines, manage performance and security, and support reliable, production-grade ML systems in cloud-native environments.

Foundations of Kubernetes for MLOps
• Role of Kubernetes in modern MLOps architectures
• Containers, pods, services, and namespaces for ML workloads
• Differences between application workloads and ML workloads
• Designing Kubernetes-native ML systems

Containerizing Machine Learning Workloads
• Packaging ML models and dependencies into containers
• Managing model artifacts and images
• Environment consistency across training and inference
• Best practices for ML container design

Deploying ML Models on Kubernetes
• Serving ML models as Kubernetes services
• Inference endpoints and API management
• Resource requests, limits, and scheduling
• Managing multiple model versions

Orchestrating ML Pipelines with Kubernetes
• Training jobs and batch processing on Kubernetes
• Workflow orchestration concepts for ML
• Managing data access and storage
• Automating retraining and deployment pipelines

Scalability and Performance Management
• Autoscaling inference workloads
• Handling traffic spikes and latency
• GPU and accelerator scheduling concepts
• Optimizing performance and cost

Monitoring and Observability for ML Systems
• Monitoring infrastructure and ML metrics
• Logging and tracing ML workloads
• Detecting failures and performance degradation
• Supporting reliable production ML systems

Security, Governance, and Reliability
• Securing ML workloads and data on Kubernetes
• Access control, secrets management, and isolation
• Model governance and auditability
• Reliability and fault tolerance strategies

Managing Drift and Continuous Improvement
• Monitoring data and concept drift
• Triggering retraining workflows
• Continuous delivery for ML models
• Maintaining long-term model performance

Scaling MLOps Platforms with Kubernetes
• Multi-tenant ML platforms
• Operating ML at organizational scale
• Cost management and optimization
• Building a Kubernetes-based MLOps roadmap

• MLOps fundamentals
• Kubernetes basics for ML
• Containerized ML workflows
• ML model deployment concepts
• CI/CD automation techniques
• Model monitoring workflows
• Scalable ML infrastructure concepts
• Kubernetes orchestration basics
• Security and governance considerations
• Practical MLOps with Kubernetes scenarios

• Kubernetes-based ML deployment scenario analysis
• MLOps pipeline orchestration exercise
• Monitoring and governance assessment
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in MLOps with Kubernetes Training, validating their expertise in deploying, orchestrating, securing, and scaling machine learning systems using Kubernetes for production MLOps environments.