Virtual Training
Data Infrastructure and Database Technologies
MLOps: Operationalising Machine Learning Models Online Course
Join our virtual, live instructor-led session and master MLOps: Operationalising Machine Learning Models Training from anywhere in the world.
5 Days
Duration
Live Online
Delivery
7 Dates
Available
Certificate
Included
Master MLOps to deploy reliable models, automate ML pipelines, and monitor production performance through practical operational workflows.
Upcoming Virtual Training Schedules
Join from anywhere in the world with our live instructor-led sessions
| Code | Start Date | End Date | Duration | Fee | |
|---|---|---|---|---|---|
| MLO-01 | Mon - Fri (5 Days) | USD 850 | Reserve my seat → Register my team → | ||
| MLO-01 | Weekend (4 Weeks) | USD 850 | Reserve my seat → Register my team → | ||
| MLO-01 | Mon - Fri (5 Days) | USD 850 | Reserve my seat → Register my team → | ||
| MLO-01 | Mon - Fri (5 Days) | USD 850 | Reserve my seat → Register my team → | ||
| MLO-01 | Weekend (4 Weeks) | USD 850 | Reserve my seat → Register my team → | ||
| MLO-01 | Mon - Fri (5 Days) | USD 850 | Reserve my seat → Register my team → |
Here's What You'll Learn
Each module tackles real challenges you face in your role
1
MLOps Foundations and Lifecycle
2
Version Control and Experiment Tracking
3
Data Pipelines and Feature Governance
4
Containerization and Workflow Automation
5
Kubernetes Deployment Patterns
6
Monitoring and Drift Detection
7
Governance, Reporting, and Roadmaps
Market-specific guidance for Kuwait
A country-aware view of the pressures, proof points, and practical tools that shape how this course applies locally.
Tools and platforms relevant to this field
4Field-relevant examples that may be featured in training where they support the confirmed scope. Exact coverage depends on participant needs and delivery format.
-
MLflow DatabricksTo track experiments, manage model versions, and support a model registry workflow for reproducible deployments.
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Docker DockerTo package model services and dependencies consistently across development, testing, and production environments.
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Kubernetes Cloud Native Computing FoundationTo orchestrate containerised model services, handle scaling, and support resilient production deployments.
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GitHub Actions GitHubTo automate training, testing, validation, and deployment steps in CI/CD pipelines.
Where this course runs
MLOps: Operationalising Machine Learning Models Training is delivered in the cities below — pick the one that fits your schedule.
