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
12 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 | 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 | 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 | 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 China
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.
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MLflow DatabricksUsed to track experiments, manage model versions, and support a model registry workflow for reproducible deployment.
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Docker Docker, Inc.Used to package model code and dependencies into portable containers so training and inference behave consistently across environments.
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Kubernetes CNCFUsed to run, scale, and update model-serving services with controlled rollout and rollback behavior.
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GitHub Actions GitHub, Inc.Used to automate testing, packaging, and deployment steps in CI/CD workflows for machine learning systems.
Where this course runs
MLOps: Operationalising Machine Learning Models Training is delivered in the cities below — pick the one that fits your schedule.
