MLOps Training and Certification in Ontario

MLOps Training and Certification in Ontario, Canada

Participants will explore the lifecycle of machine learning models, including deployment, monitoring, scaling, and governance.

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MLOps Training and Certification is a professional training program delivered by ProgNXT in Ontario, Canada. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this MLOps Training and Certification course with hands-on, instructor-led sessions. This training provides an in-depth understanding of MLOps (Machine Learning Operations), an essential discipline that integrates 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: CNQ22
21 Hrs
Last Updated: April 2026

Course Rating5/5

This training provides an in-depth understanding of MLOps (Machine Learning Operations), an essential discipline that integrates machine learning model development and operational workflows. Participants will explore the lifecycle of machine learning models, including deployment, monitoring, scaling, and governance. Hands-on sessions focus on leveraging tools such as Docker, Kubernetes, Git, CI/CD pipelines, and cloud platforms for automating and managing ML workflows. By the end of the course, participants will gain practical skills to implement robust and scalable MLOps practices.

Welcome to the official MLOps Training and Certification 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 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Participants should have:

Basic knowledge of machine learning concepts and frameworks (e.g., TensorFlow, PyTorch, or Scikit-learn).
Familiarity with Python programming.
A basic understanding of version control systems (e.g., Git).
Exposure to Linux commands and cloud computing is helpful but not mandatory.

By completing this training, participants will:

Understand the fundamentals of MLOps and its role in ML lifecycle management.
Gain insights into automating workflows for model development, deployment, and monitoring.
Learn to implement CI/CD pipelines tailored for machine learning projects.
Explore tools and frameworks for versioning, containerization, and orchestration in MLOps.
Develop the ability to monitor, retrain, and govern ML models effectively.
Apply MLOps best practices in real-world scenarios.

Upon completion, participants will:

Master MLOps workflows and automation processes.
Gain hands-on experience with tools like Git, Docker, Kubernetes, and cloud platforms.
Build, deploy, and monitor ML models in production environments.
Learn to design CI/CD pipelines for machine learning.
Understand model retraining and governance practices.
Develop strategies for scalability and reproducibility in ML projects.

Participants will be assessed on:

MLOps concepts and their application in the ML lifecycle.
Building and deploying containerized ML models using Docker.
Designing CI/CD pipelines for automated model deployment.
Managing and orchestrating models using Kubernetes.
Monitoring deployed models for performance and drift.
Applying best practices in real-world MLOps workflows.

Participants will be awarded the ProgNXT Certificate of Completion upon successful completion of the training, validating their expertise in MLOps and their readiness to implement it in production environments.

Introduction to MLOps and ML Lifecycle

Session 1: Understanding MLOps
- What is MLOps?
- Benefits of MLOps in ML workflows.
- Overview of the ML lifecycle and challenges in production.
Session 2: Tools and Technologies for MLOps
- Key tools: Git, Docker, Kubernetes, Jenkins, and MLflow.
- Overview of cloud platforms for MLOps (e.g., AWS, Azure, GCP).
Session 3: Data Preparation and Feature Engineering
- Automating data preprocessing workflows.
- Feature versioning and management strategies.

Model Deployment and CI/CD Pipelines

Session 4: Containerization for Machine Learning Models
- Introduction to Docker for ML workflows.
- Building and managing Docker images for ML models.
Session 5: Continuous Integration and Deployment
- Understanding CI/CD principles for MLOps.
- Designing and implementing CI/CD pipelines.
Session 6: Orchestration with Kubernetes
- Introduction to Kubernetes for scaling ML models.
- Deploying and managing ML models in Kubernetes clusters.

Monitoring, Retraining, and Governance

Session 7: Monitoring and Managing Deployed Models
- Setting up model monitoring frameworks (e.g., Prometheus, Grafana).
- Detecting model drift and performance degradation.
Session 8: Retraining and Model Governance
- Automating model retraining workflows.
- Versioning models with MLflow or DVC.
- Governance practices for ML pipelines.
Session 9: Course Wrap-Up
- Q&A session and best practices for implementing MLOps.

This MLOps training equips participants with the skills and tools needed to operationalize machine learning workflows effectively. With a blend of theoretical insights and hands-on exercises, this training ensures that participants are ready to manage and scale ML models in real-world production environments.

Participants will receive:

Comprehensive course slides covering theory and practical examples.
Pre-configured Jupyter notebooks and hands-on exercise scripts.
Sample datasets for MLOps use cases.
Tutorials and documentation for tools like Docker, Kubernetes, and CI/CD pipelines.
Access to cloud-based labs for practice.
Case studies and project templates for end-to-end MLOps implementation.

Participants will be evaluated through a hands-on project and a final quiz.
ProgNXT Certificate of completion will be awarded to participants who successfully complete the assessments.

MLOps Training and Certification Events in ontario

Online Events

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More Upcoming Session Dates

October 2026

🌐 Online Instructor-Led

28th Oct 2026→30th Oct 2026

November 2026

📍 Ontario, Canada

5th Nov 2026→7th Nov 2026

🌐 Online Instructor-Led

6th Nov 2026→8th Nov 2026

🌐 Online Instructor-Led

18th Nov 2026→20th Nov 2026

📍 Ontario, Canada

20th Nov 2026→22nd Nov 2026

December 2026

📍 Ontario, Canada

2nd Dec 2026→4th Dec 2026

🌐 Online Instructor-Led

3rd Dec 2026→5th Dec 2026

🌐 Online Instructor-Led

23rd Dec 2026→25th Dec 2026

📍 Ontario, Canada

25th Dec 2026→27th Dec 2026

Events for other courses in Ontario, Canada

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MLOps Training and Certification in Ontario