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LILet’s see if I can cover the ML pipeline in 60 seconds ⏰😅
Machine learning isn’t just training a model. A production ML lifecycle typically looks like this:
1️⃣ Define the problem & objective
2️⃣ Collect and (if needed) label data
3️⃣ Split into train / validation / test sets
4️⃣ Data preprocessing & feature engineering
5️⃣ Train the model (forward pass + backpropagation in deep learning)
6️⃣ Evaluate on held-out data to measure generalization
7️⃣ Hyperparameter tuning (learning rate, architecture, etc.)
8️⃣ Final testing before release
9️⃣ Deploy (batch inference or real-time serving behind an API)
🔟 Monitor for data drift, concept drift, latency, cost, and reliability
1️⃣1️⃣ Retrain when performance degrades
Training updates weights.
Evaluation measures performance.
Deployment serves predictions.
Monitoring keeps the system healthy.
It’s not linear. It’s a loop.
And once you move beyond a single experiment, that loop becomes a systems problem.
At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle.
Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍
@lindavivah
