Abstract: The example embodiments are directed to a system and method for deploying a machine learning model using a parse-free memory allocation. In one example, the method may include one or more of receiving a request to deploy a machine learning model, in response to receiving the request, creating a memory map comprising a mapping of a data structure for storing an unpacked flat representation of the machine learning model, allocating a contiguous block of memory of the data structure that is mapped by the memory map, loading data blocks of the unpacked flat representation of the machine learning model into the allocated contiguous blocks of memory of the data structure, and storing an offset associated with the contiguous block of memory in storage.

Abstract: Various embodiments provide a copy-on-write data frame (CoW-DF) that permits lightweight copies of a data frame, where the copies comprise memory allocation for only changed portions of a data frame. A CoW-DF may have semantics of a data frame, and a CoW-DF may appear and behave like a traditional data frame copy, while on the backend of a CoW-DF, only data differences created by modifications to a data frame may be maintained, rather than whole copies of a modified data frame. For various embodiments, the CoW concept is applied to other types of data structures, such as a column, a row, or a data frame value.

Abstract: Various embodiments provide systems and methods for an ensemble of decision trees that assists in selection of a predicted action to achieve a desired outcome based on an input comprising a set of feature values, such as an input feature vector for a particular instance. The predicted action may represent an optimal or best action, from the plurality of possible actions, for achieving the desired outcome.

Abstract: Various embodiments provide a copy-on-write data frame (CoW-DF) that permits lightweight copies of a data frame, where the copies comprise memory allocation for only changed portions of a data frame. A CoW-DF may have semantics of a data frame, and a CoW-DF may appear and behave like a traditional data frame copy, while on the backend of a CoW-DF, only data differences created by modifications to a data frame may be maintained, rather than whole copies of a modified data frame. For various embodiments, the CoW concept is applied to other types of data structures, such as a column, a row, or a data frame value.

Abstract: Various embodiments provide a validation decision tree, which is a type of decision tree that assists in tuning hyperparameters used to build or train another decision tree, which may be a traditional decision tree. In particular, some embodiments use a validation decision tree or a validation decision tree ensemble to evaluate a space of hyperparameters for a decision tree. Additionally, some embodiments use a validation decision tree or a validation decision tree ensemble to evaluate the space of hyperparameters without need for retraining the validation decision tree or validation decision tree ensemble.

Abstract: The example embodiments are directed to a system and method for deploying a machine learning model using a parse-free memory allocation. In one example, the method may include one or more of receiving a request to deploy a machine learning model, in response to receiving the request, creating a memory map comprising a mapping of a data structure for storing an unpacked flat representation of the machine learning model, allocating a contiguous block of memory of the data structure that is mapped by the memory map, loading data blocks of the unpacked flat representation of the machine learning model into the allocated contiguous blocks of memory of the data structure, and storing an offset associated with the contiguous block of memory in storage.

Abstract: Optimization of machine intelligence utilizes a systemic process through a plurality of computer architecture manipulation techniques that take unique advantage of efficiencies therein to minimize clock cycles and memory usage. The present invention is an application of machine intelligence which overcomes speed and memory issues in learning ensembles of decision trees in a single-machine environment. Such an application of machine intelligence includes inlining relevant statements by integrating function code into a caller's code, ensuring a contiguous buffering arrangement for necessary information to be compiled, and defining and enforcing type constraints on programming interfaces that access and manipulate machine learning data sets.

Abstract: Optimization of machine intelligence utilizes a systemic process through a plurality of computer architecture manipulation techniques that take unique advantage of efficiencies therein to minimize clock cycles and memory usage. The present invention is an application of machine intelligence which overcomes speed and memory issues in learning ensembles of decision trees in a single-machine environment. Such an application of machine intelligence includes inlining relevant statements by integrating function code into a caller's code, ensuring a contiguous buffering arrangement for necessary information to be compiled, and defining and enforcing type constraints on programming interfaces that access and manipulate machine learning data sets.

Abstract: Optimization of machine intelligence utilizes a systemic process through a plurality of computer architecture manipulation techniques that take unique advantage of efficiencies therein to minimize clock cycles and memory usage. The present invention is an application of machine intelligence which overcomes speed and memory issues in learning ensembles of decision trees in a single-machine environment. Such an application of machine intelligence includes inlining relevant statements by integrating function code into a caller's code, ensuring a contiguous buffering arrangement for necessary information to be compiled, and defining and enforcing type constraints on programming interfaces that access and manipulate machine learning data sets.

Abstract: Optimization of machine intelligence utilizes a systemic process through a plurality of computer architecture manipulation techniques that take unique advantage of efficiencies therein to minimize clock cycles and memory usage. The present invention is an application of machine intelligence which overcomes speed and memory issues in learning ensembles of decision trees in a single-machine environment. Such an application of machine intelligence includes inlining relevant statements by integrating function code into a caller's code, ensuring a contiguous buffering arrangement for necessary information to be compiled, and defining and enforcing type constraints on programming interfaces that access and manipulate machine learning data sets.