Feature Store for Timeseries
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Modern feature store
Our feature store is optimised for timeseries data
Pre-packaged data
We can pre-populate your feature store with ready-to-use timeseries
Production ready
Serve features to production ML models
What is it for?
Organise timeseries data
Feature stores provide a way to organise much of the data preparation required when building machine-learning/AI models. They allow data scientists to become more productive, and ease the path between research and production.
Read more about feature stores for timeseriesDesigned for data scientists
We've focused on making our feature store easy-to-use with simple Python/REST interfaces. Get up and running with no complex software installation.
.svg)
Code Editor
import bytehub as bh
fs = bh.feature_store()
df = fs.get_timeseries(
['weather.temperature', 'sales.volume'],
from_date='2020-01-01', to_date='2020-08-01',
freq='1d'
)
df.head()
fs = bh.feature_store()
df = fs.get_timeseries(
['weather.temperature', 'sales.volume'],
from_date='2020-01-01', to_date='2020-08-01',
freq='1d'
)
df.head()
Clean and simple
We've created a simple, easy-to-use interface that fits into your existing data-science workflow
Code Editor
print(df.value[0])
{
'forecast': [0.5, 1.2, 4.5, 1.6],
'forecast_type': 'hourly'
}
{
'forecast': [0.5, 1.2, 4.5, 1.6],
'forecast_type': 'hourly'
}
Flexible
ByteHub's feature store can deal with a wide-variety of timeseries, from simple values to complex arrays and dictionary structures
Code Editor
# Get the latest feature values
df = fs.get_last(
['feature_1', 'feature_2', 'feature_3']
)
result = model.predict(df)
df = fs.get_last(
['feature_1', 'feature_2', 'feature_3']
)
result = model.predict(df)
Production-ready
Use the same API to feed the latest features into your production models
.png){kind=small}