MLOps & Model Deployment Basics
The bridge between 'we built a model' and 'the model is in production and trustworthy'. This course covers packaging models, CI/CD for machine learning, monitoring for drift, and the responsible-AI checks every deployment should clear before it touches customers.
The gap this course fills is the one between a model that works in a notebook and a model that runs in production, which is where most organisational machine learning stops. Participants cover packaging a model, serving it behind an API, versioning both code and data, and the reproducibility discipline that lets you rebuild a result months later. Monitoring gets particular attention: data drift, model drift, and the operational reality that a model degrades quietly rather than failing loudly. The course also covers the handover question of who owns a model once it is live, which is an organisational problem more often than a technical one.
HRD Corp SBL-Khas Claimable
Programme Modules
Model Packaging
Turning a notebook into a deployable artefact, containers, APIs and reproducible environments.
CI/CD for ML
Automating test, build and deploy for models, and how ML pipelines differ from software pipelines.
Monitoring & Drift
Tracking performance in production, detecting data and concept drift, and knowing when to retrain.
Responsible-AI Checks
Bias, explainability, logging and governance gates before and after a model goes live.
Key Outcomes
- Package a model for deployment
- Describe CI/CD for ML
- Monitor models and detect drift
- Apply responsible-AI checks pre-launch
Training Mode Physical / Online / Hybrid / e-learning
HRD Corp SBL-Khas Claimable
Duration 1 Day
Training Hours 9:00 AM to 5:00 PM
Level Intermediate to Advanced
Certificate None, Orbix own course. Delivered as an HRD Corp claimable workshop with an Orbix certificate of completion.