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 Sweden
From your market
Real tools and case studies from Sweden — taught against the configurations and outcomes you'll actually encounter.
Industry Tools and Platforms Featured in this Training
6-
MLflow DatabricksExperiment tracking, model registry, and lifecycle management for reproducible model promotion.
-
Docker Docker, Inc.Package training and serving environments so models run consistently across laptops, CI, and production.
-
Kubernetes Cloud Native Computing FoundationRun scalable model-serving and batch-inference workloads with controlled rollout and rollback.
-
GitHub Actions GitHubAutomate testing, builds, and deployment gates for ML pipelines.
-
Prometheus Prometheus AuthorsCollect operational metrics from model services and supporting infrastructure.
-
Grafana Grafana LabsVisualize model latency, error rates, drift signals, and service health in dashboards.
Where this course runs
MLOps: Operationalising Machine Learning Models Training is delivered in the cities below — pick the one that fits your schedule.
