Export spark feature transformation pipeline to a file
PMML, Mleap, PFA currently only support row based transformations. None of them support frame based transformations like aggregates or groupby or join. What is the recommended way to export a spark pipeline consisting of these operations.
apache-spark apache-spark-sql pmml mleap
asked Nov 19 '18 at 17:41
GowravGowrav
389417
add a comment |
PMML, Mleap, PFA currently only support row based transformations. None of them support frame based transformations like aggregates or groupby or join. What is the recommended way to export a spark pipeline consisting of these operations.
apache-spark apache-spark-sql pmml mleap
asked Nov 19 '18 at 17:41
GowravGowrav
389417
add a comment |
PMML, Mleap, PFA currently only support row based transformations. None of them support frame based transformations like aggregates or groupby or join. What is the recommended way to export a spark pipeline consisting of these operations.
apache-spark apache-spark-sql pmml mleap
asked Nov 19 '18 at 17:41
GowravGowrav
389417
PMML, Mleap, PFA currently only support row based transformations. None of them support frame based transformations like aggregates or groupby or join. What is the recommended way to export a spark pipeline consisting of these operations.
apache-spark apache-spark-sql pmml mleap
apache-spark apache-spark-sql pmml mleap
asked Nov 19 '18 at 17:41
GowravGowrav
389417
asked Nov 19 '18 at 17:41
GowravGowrav
389417
edited Nov 23 '18 at 18:06
Gowrav
asked Nov 19 '18 at 17:41
GowravGowrav
389417
asked Nov 19 '18 at 17:41
GowravGowrav
389417
asked Nov 19 '18 at 17:41
GowravGowrav
389417
389417
add a comment |
add a comment |
1 Answer
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PMML and PFA are standards for representing machine learning models, not data processing pipelines. A machine learning model takes in a data record, performs some computation on it, and emits an output data record. So by definition, you are working with a single isolated data record, not a collection/frame/matrix of data records.
If you need to represent complete data processing pipelines (where the ML model is just part of the workflow) then you need to look for other/combined standards. Perhaps SQL paired with PMML would be a good choice. The idea is that you want to perform data aggregation outside of the ML model, not inside it (eg. a SQL database will be much better at it than any PMML or PFA runtime).
answered Nov 26 '18 at 10:27
user1808924user1808924
1,9652913
1
PFA and MLeap are not restricted only for machine learning models. As per DMG, PFA is an emerging standard for statistical models and data transformation engines. Also within the mleap development, there are discussion regarding converting the existing row based transformation to frame based. Refer here
– Gowrav
Nov 26 '18 at 19:26
In this context, "data transformation" means feature engineering, not re-implementing SQL standard. For example, PMML comes with built-in aggregate functions (dmg.org/pmml/v4-3/Transformations.html#xsdElement_Aggregate) but their scope is limited to that one data record (not a database).
– user1808924
Nov 26 '18 at 20:53
To elaborate: "data transformation" != "data query".
– user1808924
Nov 26 '18 at 20:58
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
PMML and PFA are standards for representing machine learning models, not data processing pipelines. A machine learning model takes in a data record, performs some computation on it, and emits an output data record. So by definition, you are working with a single isolated data record, not a collection/frame/matrix of data records.
If you need to represent complete data processing pipelines (where the ML model is just part of the workflow) then you need to look for other/combined standards. Perhaps SQL paired with PMML would be a good choice. The idea is that you want to perform data aggregation outside of the ML model, not inside it (eg. a SQL database will be much better at it than any PMML or PFA runtime).
answered Nov 26 '18 at 10:27
user1808924user1808924
1,9652913
1
PFA and MLeap are not restricted only for machine learning models. As per DMG, PFA is an emerging standard for statistical models and data transformation engines. Also within the mleap development, there are discussion regarding converting the existing row based transformation to frame based. Refer here
– Gowrav
Nov 26 '18 at 19:26
In this context, "data transformation" means feature engineering, not re-implementing SQL standard. For example, PMML comes with built-in aggregate functions (dmg.org/pmml/v4-3/Transformations.html#xsdElement_Aggregate) but their scope is limited to that one data record (not a database).
– user1808924
Nov 26 '18 at 20:53
To elaborate: "data transformation" != "data query".
– user1808924
Nov 26 '18 at 20:58
add a comment |
PMML and PFA are standards for representing machine learning models, not data processing pipelines. A machine learning model takes in a data record, performs some computation on it, and emits an output data record. So by definition, you are working with a single isolated data record, not a collection/frame/matrix of data records.
If you need to represent complete data processing pipelines (where the ML model is just part of the workflow) then you need to look for other/combined standards. Perhaps SQL paired with PMML would be a good choice. The idea is that you want to perform data aggregation outside of the ML model, not inside it (eg. a SQL database will be much better at it than any PMML or PFA runtime).
answered Nov 26 '18 at 10:27
user1808924user1808924
1,9652913
1
PFA and MLeap are not restricted only for machine learning models. As per DMG, PFA is an emerging standard for statistical models and data transformation engines. Also within the mleap development, there are discussion regarding converting the existing row based transformation to frame based. Refer here
– Gowrav
Nov 26 '18 at 19:26
In this context, "data transformation" means feature engineering, not re-implementing SQL standard. For example, PMML comes with built-in aggregate functions (dmg.org/pmml/v4-3/Transformations.html#xsdElement_Aggregate) but their scope is limited to that one data record (not a database).
– user1808924
Nov 26 '18 at 20:53
To elaborate: "data transformation" != "data query".
– user1808924
Nov 26 '18 at 20:58
add a comment |
PMML and PFA are standards for representing machine learning models, not data processing pipelines. A machine learning model takes in a data record, performs some computation on it, and emits an output data record. So by definition, you are working with a single isolated data record, not a collection/frame/matrix of data records.
If you need to represent complete data processing pipelines (where the ML model is just part of the workflow) then you need to look for other/combined standards. Perhaps SQL paired with PMML would be a good choice. The idea is that you want to perform data aggregation outside of the ML model, not inside it (eg. a SQL database will be much better at it than any PMML or PFA runtime).
answered Nov 26 '18 at 10:27
user1808924user1808924
1,9652913
PMML and PFA are standards for representing machine learning models, not data processing pipelines. A machine learning model takes in a data record, performs some computation on it, and emits an output data record. So by definition, you are working with a single isolated data record, not a collection/frame/matrix of data records.
If you need to represent complete data processing pipelines (where the ML model is just part of the workflow) then you need to look for other/combined standards. Perhaps SQL paired with PMML would be a good choice. The idea is that you want to perform data aggregation outside of the ML model, not inside it (eg. a SQL database will be much better at it than any PMML or PFA runtime).
answered Nov 26 '18 at 10:27
user1808924user1808924
1,9652913
answered Nov 26 '18 at 10:27
user1808924user1808924
1,9652913
answered Nov 26 '18 at 10:27
user1808924user1808924
1,9652913
answered Nov 26 '18 at 10:27
user1808924user1808924
1,9652913
1,9652913
1
PFA and MLeap are not restricted only for machine learning models. As per DMG, PFA is an emerging standard for statistical models and data transformation engines. Also within the mleap development, there are discussion regarding converting the existing row based transformation to frame based. Refer here
– Gowrav
Nov 26 '18 at 19:26
In this context, "data transformation" means feature engineering, not re-implementing SQL standard. For example, PMML comes with built-in aggregate functions (dmg.org/pmml/v4-3/Transformations.html#xsdElement_Aggregate) but their scope is limited to that one data record (not a database).
– user1808924
Nov 26 '18 at 20:53
To elaborate: "data transformation" != "data query".
– user1808924
Nov 26 '18 at 20:58
add a comment |
1
PFA and MLeap are not restricted only for machine learning models. As per DMG, PFA is an emerging standard for statistical models and data transformation engines. Also within the mleap development, there are discussion regarding converting the existing row based transformation to frame based. Refer here
– Gowrav
Nov 26 '18 at 19:26
In this context, "data transformation" means feature engineering, not re-implementing SQL standard. For example, PMML comes with built-in aggregate functions (dmg.org/pmml/v4-3/Transformations.html#xsdElement_Aggregate) but their scope is limited to that one data record (not a database).
– user1808924
Nov 26 '18 at 20:53
To elaborate: "data transformation" != "data query".
– user1808924
Nov 26 '18 at 20:58
1
1
PFA and MLeap are not restricted only for machine learning models. As per DMG, PFA is an emerging standard for statistical models and data transformation engines. Also within the mleap development, there are discussion regarding converting the existing row based transformation to frame based. Refer here
– Gowrav
Nov 26 '18 at 19:26
PFA and MLeap are not restricted only for machine learning models. As per DMG, PFA is an emerging standard for statistical models and data transformation engines. Also within the mleap development, there are discussion regarding converting the existing row based transformation to frame based. Refer here
– Gowrav
Nov 26 '18 at 19:26
In this context, "data transformation" means feature engineering, not re-implementing SQL standard. For example, PMML comes with built-in aggregate functions (dmg.org/pmml/v4-3/Transformations.html#xsdElement_Aggregate) but their scope is limited to that one data record (not a database).
– user1808924
Nov 26 '18 at 20:53
In this context, "data transformation" means feature engineering, not re-implementing SQL standard. For example, PMML comes with built-in aggregate functions (dmg.org/pmml/v4-3/Transformations.html#xsdElement_Aggregate) but their scope is limited to that one data record (not a database).
– user1808924
Nov 26 '18 at 20:53
To elaborate: "data transformation" != "data query".
– user1808924
Nov 26 '18 at 20:58
To elaborate: "data transformation" != "data query".
– user1808924
Nov 26 '18 at 20:58
add a comment |
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