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@@ -1,7 +1,7 @@ | |||
| 1 | 1 | --- | |
| 2 | 2 | title: "Feast + MLflow + Kubeflow: A Unified AI/ML Lifecycle" | |
| 3 | 3 | description: Learn how to use Feast, MLflow, and Kubeflow to power your AI/ML Lifecycle | |
| 4 | - date: 2026-02-23 | ||
| 4 | + date: 2026-03-09 | ||
| 5 | 5 | authors: ["Francisco Javier Arceo", "Nikhil Kathole"] | |
| 6 | 6 | --- | |
| 7 | 7 | ||
@@ -366,6 +366,11 @@ This is exactly the kind of insight MLflow's comparison interface is built for. | |||
| 366 | 366 | <p><em>MLflow's comparison view showing three runs side by side with different feature subsets. The "Show diff only" toggle highlights how the <code>features</code> parameter varies across runs, making it easy to identify which combination of Feast features produces the best results.</em></p> | |
| 367 | 367 | </div> | |
| 368 | 368 | ||
| 369 | + <div style="text-align: center; margin: 20px 0;"> | ||
| 370 | + <img src="/images/blog/mlflow-feast-feature-selection-metrics.png" alt="MLflow metric charts showing accuracy, AUC, F1, precision, and recall grouped by num_features across three feature subsets" loading="lazy" style="max-width: 100%; border: 1px solid #e0e0e0; border-radius: 8px;"> | ||
| 371 | + <p><em>MLflow's metric charts view visualizing accuracy, AUC, F1, precision, and recall across all feature selection runs, grouped by <code>num_features</code>. This chart makes it easy to spot how model performance changes as more Feast features are included.</em></p> | ||
| 372 | + </div> | ||
| 373 | + | ||
| 369 | 374 | Once you have identified the winning subset, the Feast registry ensures that only those features need to be materialized into the online store for production serving. | |
| 370 | 375 | ||
| 371 | 376 | ### Hyperparameter sweeps | |
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