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 Türkiye
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
6Field-relevant examples that may be featured in training where they support the confirmed scope. Exact coverage depends on participant needs and delivery format.
-
MLflow DatabricksUsed to track experiments, manage model versions, and support a model registry workflow for reproducible promotion from development to production.
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Docker Docker, Inc.Used to package models and their runtime dependencies consistently so they behave the same in development, testing, and production.
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Kubernetes Cloud Native Computing FoundationUsed to orchestrate containerised model services, handle scaling, and support resilient deployment of ML applications.
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GitHub Actions GitHubUsed to automate testing, packaging, and deployment steps in CI/CD pipelines for ML code and model artefacts.
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Prometheus The Linux FoundationUsed to collect operational metrics for deployed model services and alert teams when performance or infrastructure signals drift.
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Grafana Grafana LabsUsed to visualise model and pipeline metrics on monitoring dashboards for operational oversight.
Where this course runs
MLOps: Operationalising Machine Learning Models Training is delivered in the cities below — pick the one that fits your schedule.
