Machine learning doesn’t fail because models are bad.
It fails because quality is ignored across the lifecycle.
As a Quality Engineer, this realization was eye‑opening.
ML isn’t just training a model — it’s a continuous lifecycle:
business goals
problem framing
data processing
model development
deployment
monitoring
retraining
And QA has a role in every single stage — from defining testable goals to detecting data drift and regression issues in retrained models.
If ML systems are probabilistic, data-driven, and constantly evolving…
then testing must focus on behavior, not just logic.
👉 Read the full deep dive on Hashnode:
https://hemaai.hashnode.dev/the-machine-learning-development-lifecycle-and-why-qa-is-critical-at-every-stage
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