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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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Fix code examples in PyTorch blog post - add missing imports and defi… 
…ne variables

Co-authored-by: franciscojavierarceo <4163062+franciscojavierarceo@users.noreply.github.com>
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@franciscojavierarceo
Copilot and franciscojavierarceo committed Jan 26, 2026
commit bf7f923577c4a16779b2ad52f6fad5a6c958c326
14 changes: 11 additions & 3 deletions infra/website/docs/blog/feast-joins-pytorch-ecosystem.md
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Expand Up @@ -37,6 +37,8 @@ Feast integrates seamlessly with PyTorch workflows, whether you're training mode
from feast import FeatureStore
import torch
from torch.utils.data import Dataset, DataLoader
import pandas as pd
from datetime import datetime

# Initialize Feast
store = FeatureStore(repo_path=".")
Expand All @@ -58,7 +60,7 @@ training_df = store.get_historical_features(

# Create PyTorch Dataset
class FeatureDataset(Dataset):
def __init__(self, df):
def __init__(self, df, feature_cols):
self.features = torch.tensor(df[feature_cols].values, dtype=torch.float32)
self.labels = torch.tensor(df['label'].values, dtype=torch.float32)

Expand All @@ -69,10 +71,16 @@ class FeatureDataset(Dataset):
return self.features[idx], self.labels[idx]

# Train your PyTorch model
dataset = FeatureDataset(training_df)
feature_cols = ['age', 'activity_score', 'popularity']
dataset = FeatureDataset(training_df, feature_cols)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

model = YourPyTorchModel()
# Define and train your model
model = torch.nn.Sequential(
torch.nn.Linear(len(feature_cols), 64),
torch.nn.ReLU(),
torch.nn.Linear(64, 1)
)
# Training loop...
```

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