Can the AI model maintain continuous access to data sources after deployment?
Can the AI model maintain continuous access to data sources after deployment?
- Will you use the output from other models to feed your model again (looping)? Or will you use other sources?
- Your AI system may rely on internal pipelines or third-party data sources. If any of these become unavailable, the model may stop functioning or deliver inaccurate results.
- This includes scenarios like discontinued APIs, broken survey collection tools, or changes in upstream system outputs.
If you answered No then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Consider how the model will keep learning.
- Identify critical data dependencies and define fallback mechanisms.
- Assess whether key data sources are stable and under your control or subject to third-party risks.
- Monitor availability of inputs to catch outages early.
- Imagine you planned to feed your model with input obtained by mining surveys and it appears these surveys contain a lot of free text fields. To prepare that data and avoid issues (bias, inaccuracies, etc) you might need extra time. Consider these types of scenarios that could impact the whole life cycle of your system.