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There are 4 versions of this glossary term.

What is a MOJO?

MOJO (stands for Model Object, Optimized) [1] is a standalone, low-latency model object designed to be easily embeddable in production environments.

In Driverless AI, the MOJO Model is combined with a feature engineering pipeline to create a MOJO scoring pipeline that can be executed in Java Runtime or C++ Runtime. The Driverless AI MOJO Model could be XGBoost, LightGBM, GLM, Decision Tree, TensorFlow, FTRL and even other models. A valid Driverless AI license key in required to run the MOJO scoring pipeline. The Driverless AI MOJO scoring pipeline can be executed using the mojo2-runtime.jar, mojo2 py runtime, mojo2 r runtime or mojo2 C lib runtime. Once you have the Driverless AI license key, the MOJO scoring pipeline can be run practically anywhere [1].


In H2O-3, the MOJO does not combine with a feature engineering pipeline and is just a scoring pipeline that can be executed in Java Runtime using h2o-genmodel.jar, h2o py module or h2o r library. H2O-3 supports supervised and unsupervised algorithms for the MOJO model. H2O-3 is open source under the Apache License 2.0 and does not require a license key to execute the H2O-3 MOJO [2].

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Revised By: Jo-fai Chow Revised On: Jun 8, 2020 7:25 AM
Characters Edited: 991 Total: 8073

What is a MOJO?

MOJO (stands for Model Object, Optimized) [1] is a standalone, low-latency model object designed to be easily embeddable in production environments.

In Driverless AI, the MOJO Model is combined with a feature engineering pipeline to create a MOJO Scoring Pipeline that can be executed in Java Runtime or C++ Runtime. The Driverless AI MOJO Model could be XGBoost, LightGBM, GLM, Decision Tree, TensorFlow, FTRL and even other models. Driverless AI must be purchased to get the license key in order to execute the MOJO Scoring Pipeline. The Driverless AI MOJO Scoring Pipeline can be executed using the mojo2-runtime.jar, mojo2 py runtime, mojo2 r runtime or mojo2 C lib runtime. Once you have the Driverless AI license key, the MOJO Scoring Pipeline can be run practically anywhere [1].


In H2O-3, the MOJO Model does not combine with a feature engineering pipeline and is just a Model that can be executed in Java Runtime using h2o-genmodel.jar, h2o py module or h2o r library. H2O-3 supports supervised and unsupervised algorithms for the MOJO model. H2O-3 is open source under the Apache License 2.0 and does not require a license key to execute the H2O-3 MOJO Model [2].

Resources

Tutorial

Videos

Revised By: Rafael Coss Revised On: Jun 7, 2020 7:43 PM
Characters Edited: 115 Total: 7082

MOJO (stands for Model Object, Optimized) [1] is a standalone, low-latency model object designed to be easily embeddable in production environments.

In Driverless AI, the MOJO Model is combined with a feature engineering pipeline to create a MOJO Scoring Pipeline that can be executed in Java Runtime or C++ Runtime. The Driverless AI MOJO Model could be XGBoost, LightGBM, GLM, Decision Tree, TensorFlow, FTRL and even other models. Driverless AI must be purchased to get the license key in order to execute the MOJO Scoring Pipeline. The Driverless AI MOJO Scoring Pipeline can be executed using the mojo2-runtime.jar, mojo2 py runtime, mojo2 r runtime or mojo2 C lib runtime. Once you have the Driverless AI license key, the MOJO Scoring Pipeline can be run practically anywhere [1].


In H2O-3, the MOJO Model does not combine with a feature engineering pipeline and is just a Model that can be executed in Java Runtime using h2o-genmodel.jar, h2o py module or h2o r library. H2O-3 supports supervised and unsupervised algorithms for the MOJO model. H2O-3 is open source under the Apache License 2.0 and does not require a license key to execute the H2O-3 MOJO Model [2].

Resources

Tutorial

Videos

Revised By: Jo-fai Chow Revised On: Jun 5, 2020 10:20 AM
Characters Edited: -77 Total: 6967

MOJO (stands for Model Object, Optimized) [1] is a standalone, low-latency model object designed to be easily embeddable in production environments.

In Driverless AI, the MOJO Model is combined with a feature engineering pipeline to create a MOJO Scoring Pipeline that can be executed in Java Runtime or C++ Runtime. The Driverless AI MOJO Model could be XGBoost, LightGBM, GLM, Decision Tree, TensorFlow, FTRL and even other models. Driverless AI must be purchased to get the license key in order to execute the MOJO Scoring Pipeline. The Driverless AI MOJO Scoring Pipeline can be executed using the mojo2-runtime.jar, mojo2 py runtime, mojo2 r runtime or mojo2 C lib runtime. Once you have the Driverless AI license key, the MOJO Scoring Pipeline can be run practically anywhere [1].


In H2O-3, the MOJO Model does not combine with a feature engineering pipeline and is just a Model that can be executed in Java Runtime using h2o-genmodel.jar, h2o py module or h2o r library. H2O-3 supports supervised and unsupervised algorithms for the MOJO model. H2O-3 is open source under the Apache License 2.0 and does not require a license key to execute the H2O-3 MOJO Model [2].

Resources

Tutorial

Videos

Revised By: Jo-fai Chow Revised On: Jun 5, 2020 9:42 AM
Characters Edited: 0 Total: 7044