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 Indonesia
From your market
Real tools and case studies from Indonesia — taught against the configurations and outcomes you'll actually encounter.
Industry Tools and Platforms Featured in this Training
6-
MLflow DatabricksUsed for experiment tracking, model versioning, and model registry workflows in production ML pipelines.
-
Docker Docker, Inc.Used to package ML applications and dependencies so models run consistently across development, test, and production environments.
-
Kubernetes The Kubernetes AuthorsUsed to orchestrate containerized model services and support scalable, repeatable deployments.
-
GitHub Actions GitHub, Inc.Used to automate CI/CD workflows for testing, packaging, and deploying ML code and model services.
-
Apache Airflow Apache Software FoundationUsed to schedule and manage ML pipelines, including data preparation, training, and retraining tasks.
-
Prometheus The Prometheus AuthorsUsed to collect operational metrics from model services and supporting infrastructure for monitoring.
Where this course runs
MLOps: Operationalising Machine Learning Models Training is delivered in the cities below — pick the one that fits your schedule.
