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
Built for Switzerland
From your market
Real tools and case studies from Switzerland — taught against the configurations and outcomes you'll actually encounter.
Industry Tools and Platforms Featured in this Training
6-
MLflow DatabricksUsed to track experiments, register models, and manage reproducible promotion from training to deployment.
-
Docker DockerUsed to package model code, dependencies, and runtime behavior into portable containers for consistent deployment.
-
Kubernetes Cloud Native Computing FoundationUsed to orchestrate containerized model services and support scaling, rollouts, and service reliability.
-
GitHub Actions GitHubUsed to automate testing, validation, build, and deployment steps in CI/CD workflows.
-
Prometheus PrometheusUsed to collect service and model runtime metrics for operational monitoring.
-
Grafana Grafana LabsUsed to visualize model, pipeline, and infrastructure metrics in monitoring dashboards.
Where this course runs
MLOps: Operationalising Machine Learning Models Training is delivered in the cities below — pick the one that fits your schedule.
