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Merged
franciscojavierarceo merged 6 commits into from
Jan 27, 2026
Merged

feat: Add blog post for PyTorch ecosystem announcement #5906

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ecda991
Initial plan
Copilot Jan 26, 2026
5f7b291
Add blog post about Feast joining PyTorch ecosystem
Copilot Jan 26, 2026
bf7f923
Fix code examples in PyTorch blog post - add missing imports and defi…
Copilot Jan 26, 2026
5854b8c
Improve code examples with clarifying comments
Copilot Jan 26, 2026
76f4f27
Apply suggestion from @franciscojavierarceo
franciscojavierarceo Jan 27, 2026
c197d19
Merge branch 'master' into copilot/add-blog-post-pytorch-ecosystem
franciscojavierarceo Jan 27, 2026
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Improve code examples with clarifying comments 
Co-authored-by: franciscojavierarceo <4163062+franciscojavierarceo@users.noreply.github.com>
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@franciscojavierarceo
Copilot and franciscojavierarceo committed Jan 26, 2026
commit 5854b8c80570f055efd4a64c46c55b9c8a20ee6d
15 changes: 11 additions & 4 deletions infra/website/docs/blog/feast-joins-pytorch-ecosystem.md
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Expand Up @@ -58,6 +58,9 @@ training_df = store.get_historical_features(
]
).to_df()

# Add your labels (from your training data source)
# training_df['label'] = ...

# Create PyTorch Dataset
class FeatureDataset(Dataset):
def __init__(self, df, feature_cols):
Expand Down Expand Up @@ -90,7 +93,7 @@ Once your PyTorch model is trained, Feast makes it easy to serve features in pro

```python
# Get online features for real-time prediction
features = store.get_online_features(
online_features = store.get_online_features(
entity_rows=[{"user_id": 1001}],
features=[
"user_features:age",
Expand All @@ -100,9 +103,13 @@ features = store.get_online_features(
).to_dict()

# Convert to tensor and run inference
feature_tensor = torch.tensor([features['age'][0],
features['activity_score'][0],
features['popularity'][0]])
# Note: Feature keys in the dict will use the format "feature_view__feature_name"
feature_values = [
online_features['age'][0],
online_features['activity_score'][0],
online_features['popularity'][0]
]
feature_tensor = torch.tensor(feature_values, dtype=torch.float32)

with torch.no_grad():
prediction = model(feature_tensor)
Expand Down
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