Abstract: Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation.

Abstract: Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation.

Abstract: Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation.

Abstract: A system including a master machine and a plurality of worker machines is disclosed. the master machine includes, for example, an API server configured to receive a job description; a resource allocation module configured to determine a number of virtual machines required to perform a job based on the job description; a container scheduling module configured to create a container containing the number of virtual machines required to perform the job, wherein at least two of the virtual machines in the container resides on different worker machines, and wherein each of the virtual machines is configured to run a same application to perform the job.

Abstract: A system including a master machine and a plurality of worker machines is disclosed. The master machine includes, for example, an API server configured to receive a job description; a resource allocation module configured to determine a number of virtual machines required to perform a job based on the job description; a container scheduling module configured to create a container containing the number of virtual machines required to perform the job, wherein at least two of the virtual machines in the container resides on different worker machines, and wherein each of the virtual machines is configured to run a same application to perform the job.

Abstract: Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation.

Abstract: A system including a master machine and a plurality of worker machines is disclosed. The master machine includes, for example, an API server configured to receive a job description; a resource allocation module configured to determine a number of virtual machines required to perform a job based on the job description; a container scheduling module configured to create a container containing the number of virtual machines required to perform the job, wherein at least two of the virtual machines in the container resides on different worker machines, and wherein each of the virtual machines is configured to run a same application to perform the job.