Within the earlier a part of this collection, we explored MLOps — how it bridges the gap between machine learning and DevOps to make sure clean deployment, monitoring, and upkeep of ML fashions. However earlier than we get into how MLOps streamlines machine studying workflows, we have to perceive the Machine Studying Lifecycle.
The ML lifecycle isn’t nearly constructing a mannequin, it’s about fixing a real-world downside with an end-to-end pipeline that features information assortment, mannequin coaching, validation, deployment, and monitoring. Let’s break down these phases with sensible examples.
Earlier than writing a single line of code, step one is knowing the issue you need to remedy.
Think about we’re working in an e-commerce firm, and the administration desires to foretell buyer churn. The aim is to determine customers who’re more likely to cease buying and take proactive actions to retain them.
At this stage, key inquiries to reply embody:
– What’s the enterprise goal? (e.g., scale back churn by 10%)
– What would be the output of the mannequin? (e.g., a chance rating for every consumer)
– How will the predictions be used? (e.g., personalised low cost presents for high-risk customers)
