From the course: MLOps and Data Pipeline Orchestration for AI Systems
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The MLOps lifecycle
From the course: MLOps and Data Pipeline Orchestration for AI Systems
The MLOps lifecycle
- [Instructor] Here is how you can think of the MLOps lifecycle. Please note that this entire lifecycle has to be executed in an automated manner for quick feedback. There should be no manual processes involved in this workflow. The first thing to note here is that your machine learning model is impacted by two changes, code changes, as well as data changes. Let's discuss code changes first. If you modify the model architecture, change the feature engineering logic or the training pipeline, all of this can alter how the model learns from data. For example, if you change from a logistic regression model to a neural network, that may lead to different predictions and performance. Models are also affected by data changes. If you have new data or if you have missing or differently distributed data, all of this can shift the model's understanding and affect its accuracy. For example, a recommendation model trained on last year's user behavior may perform poorly if user preferences have…
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