MLOps on Kubernetes Training in Ontario

MLOps on Kubernetes Training in Ontario, Canada

This course focused on building, deploying, scaling, and operating machine learning systems on Kubernetes.

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MLOps on Kubernetes Training is a professional training program delivered by ProgNXT in Ontario, Canada. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this MLOps on Kubernetes Training course with hands-on, instructor-led sessions. The MLOps on Kubernetes Training is a hands-on, enterprise-grade program focused on building, deploying, scaling, and operating machine learning...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: LHE19
14 Hrs
Last Updated: April 2026

Course Rating5/5

The MLOps on Kubernetes Training is a hands-on, enterprise-grade program focused on building, deploying, scaling, and operating machine learning systems on Kubernetes.
Participants will learn how to operationalize ML models using containerization, CI/CD, model lifecycle management, monitoring, and scalable inference/training, enabling reliable, repeatable, and production-ready ML platforms.

Welcome to the official MLOps on Kubernetes Training certification program in Ontario, Canada. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Basic understanding of machine learning concepts
Familiarity with Python and ML workflows
Basic knowledge of containers and Docker
Introductory Kubernetes knowledge is helpful

By the end of this training, participants will be able to:

Design production-ready MLOps architectures on Kubernetes
Containerize and deploy ML training and inference workloads
Implement CI/CD for ML pipelines
Manage model versioning and lifecycle
Monitor, scale, and govern ML systems
Operate reliable ML platforms in production

Kubernetes-based ML architecture
Containerized ML workloads
ML pipeline orchestration
Model registry and lifecycle management
Scalable model serving
Monitoring, drift detection, and observability
Secure and governed ML operations

Kubernetes-based MLOps architecture
Pipeline orchestration
Model lifecycle management
Serving and scaling strategies
Monitoring and governance practices

Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion.

Introduction to MLOps & Kubernetes

What is MLOps and why it matters
Challenges of production ML systems
Why Kubernetes for ML platforms
MLOps lifecycle overview

Kubernetes Fundamentals for MLOps

Kubernetes architecture recap
Pods, Deployments, Services for ML workloads
Namespaces and resource isolation
Storage and networking basics for ML

Containerizing ML Workloads

Packaging training and inference code
Building ML-ready Docker images
Managing dependencies and environments
Image versioning and reproducibility

ML Pipelines on Kubernetes

Pipeline concepts and orchestration
Kubeflow Pipelines overview
DAG-based ML workflows
Managing pipeline artifacts and metadat

Experiment Tracking & Model Registry

MLflow tracking and experiments
Model versioning and lineage
Model registry concepts
Promoting models across environments

Training at Scale on Kubernetes

Distributed training concepts
GPU and accelerator scheduling
Resource requests and limits
Managing large-scale training job

Model Serving & Inference

Batch vs real-time inference
Deploying inference services
Canary and blue/green model deployments
Autoscaling inference workloads

CI/CD for Machine Learning

CI/CD pipelines for ML code and models
Automating training and deployment
GitOps for ML workflows
Environment promotion strategies

Data & Feature Management

Data versioning concepts
Feature stores overview
Managing training vs inference data
Data validation and quality checks

Monitoring, Drift & Observability

Monitoring model performance
Data and concept drift detection
Logging and metrics for ML systems
Alerting and incident response

Security, Governance & Compliance

Secrets and credential management
Access control for ML platforms
Model auditability and traceability
Compliance and responsible AI considerations

Cost, Performance & Optimization

Resource optimization strategies
GPU and compute cost management
Caching and reuse of artifacts
Capacity planning for ML workloads

Cloud & Hybrid MLOps Patterns

Running MLOps on cloud Kubernetes
Hybrid and multi-cluster strategies
Centralized vs decentralized ML platforms
Integration with cloud ML services

Hands-on Exercises

Summary and Conclusion

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

Participants will be evaluated based on: Daily quizzes, mock questions and query resolution. Group Discussion and Case Study Discussion. Assessment will be through hands-on labs, quizzes, and hands-on exercises.

ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

MLOps on Kubernetes Training Events in ontario

Online Events

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More Upcoming Session Dates

October 2026

📍 Ontario, Canada

20th Oct 2026→21st Oct 2026

🌐 Online Instructor-Led

26th Oct 2026→27th Oct 2026

November 2026

📍 Ontario, Canada

1st Nov 2026→2nd Nov 2026

🌐 Online Instructor-Led

2nd Nov 2026→3rd Nov 2026

🌐 Online Instructor-Led

20th Nov 2026→21st Nov 2026

📍 Ontario, Canada

21st Nov 2026→22nd Nov 2026

December 2026

📍 Ontario, Canada

10th Dec 2026→11th Dec 2026

🌐 Online Instructor-Led

11th Dec 2026→12th Dec 2026

🌐 Online Instructor-Led

21st Dec 2026→22nd Dec 2026

📍 Ontario, Canada

22nd Dec 2026→23rd Dec 2026

Events for other courses in Ontario, Canada

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MLOps on Kubernetes Training in Ontario