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We are excited to announce Feathr 1.0.0 is released, please refer to https://feathr-ai.github.io/feathr/release-announcements/v1.0.0.html for details
Full Changelog: v0.9.0...v0.10.4-rc3
Full Changelog: v0.9.0...v0.10.4-rc2
Full Changelog: v0.9.0...v0.10.4-rc1
As of v0.10.0, Feathr team is happy to introduce Online Transform (alpha). You might want to try Online Transform in the following scenarios:
Note: Only use alpha build for evaluation. Alpha build is intented for early access and feedback, it might not as stable as GA build and might be changed at any time before GA without notice. Please use alpha build at your risk.
Feathr Online Transform can be run locally with docker. Please check out details on README.
To deploy Feathr Online Transform to Azure, you need to have a AKS cluseter setup on Azure first. Then follow README to deploy helm chart to AKS cluster.
Feathr Online Transform also has a Python library for further development, check out details from the project site
Source code for Online Transform (alpha) are hosted under personal accounts, Feathr team is working on moving these source codes to the official repo but it might take some time.
We have changed the execution engine for derived features to Spark SQL so this might introduce a little bit breaking changes for users who is not running the up-to-date sample notebooks. Specifically, they might face this failure:
Preprocessed DataFrames are:
{'feature_user_age,feature_user_gift_card_balance,feature_user_has_valid_credit_card,feature_user_tax_rate': JavaObject id=o243}
Traceback (most recent call last):
File "feathr_pyspark_driver.py", line 107, in <module>
submit_spark_job(feature_names_funcs)
File "feathr_pyspark_driver.py", line 85, in submit_spark_job
py4j_feature_job.mainWithPreprocessedDataFrame(job_param_java_array, new_preprocessed_df_map)
File "/home/trusted-service-user/cluster-env/env/lib/python3.8/site-packages/py4j/java_gateway.py", line 1304, in __call__
return_value = get_return_value(
File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 117, in deco
pyspark.sql.utils.AnalysisException: Undefined function: 'toBoolean'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 1 pos 84
)
Users should change:
feature_user_purchasing_power = DerivedFeature(name="feature_user_purchasing_power",
key=user_id,
feature_type=FLOAT,
input_features=[
feature_user_gift_card_balance, feature_user_has_valid_credit_card],
transform="feature_user_gift_card_balance + if_else(toBoolean(feature_user_has_valid_credit_card), 100, 0)")to
feature_user_purchasing_power = DerivedFeature(name="feature_user_purchasing_power",
key=user_id,
feature_type=FLOAT,
input_features=[
feature_user_gift_card_balance, feature_user_has_valid_credit_card],
transform="feature_user_gift_card_balance + if(boolean(feature_user_has_valid_credit_card), 100, 0)")Full Changelog: v0.7.2...v0.8.0
Full Changelog: v0.6.0...v0.7.0
Full Changelog: v0.5.1...v0.6.0
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