Risk exposure across the ML system lifecycle

Instruction and application

Complete

Is your system ready for the real world?

Your model might be ready for deployment, but is your entire ML system ready for the real world?

Understanding compliance is just the start. To manage risk effectively, you need to recognise vulnerabilities across the entire system—from the data you rely on to the infrastructure you deploy.

System-level ML risk categories

A single model lives within a complex ecosystem: pipelines, APIs, cloud services, and business processes. Even if the model is accurate, the system can still fail.

• Vendor lock-in: Heavy reliance on a single provider limits flexibility and introduces pricing/service risks.
• Model deployment failure: Errors in scripts or misaligned environments can break production.
• Inadequate version control: Without tracking data and model versions, results are hard to reproduce.
• Missing fallback mechanisms: Lack of backup systems (e.g., human handover) leads to service outages.

Third-party and operational dependencies

No ML system operates in a vacuum. Dependencies introduce unique risks:

• External data sources: Changes in quality, availability, or licensing of third-party datasets.
• APIs and SaaS: Deprecation or outages in external services used for feature engineering.
• Physical infrastructure: Server outages or cybersecurity breaches at cloud providers.

Why it matters

Identifying these dependencies early helps you build more resilient systems and assess potential points of failure.

Mapping risks to accountability

Someone must be answerable for every risk. A responsibility matrix helps teams assign ownership at every stage.

Risk Area	Responsible Role	Example Accountability
Model bias	Model owner	Conduct fairness tests, document results.
API management	Platform lead	Monitor stability, manage failover strategies.
Data licensing	Legal team	Review agreements, ensure compliance.
Security	Security officer	Manage permissions, enforce encryption.
Monitoring	ML engineer	Respond to performance anomalies.

Action item: Spot the risks

Your team is developing an energy demand forecasting model using third-party weather APIs and market prices. Currently, there is no ownership for API changes, model versions are scattered, and there's no fallback if the API fails.

Reflection: Energy Demand Model

1. What are the top three system-level risks in this energy forecasting scenario?

Type your response here...

2. How would you assign accountability using the roles defined in the responsibility matrix?

Type your response here...