Abstract: A computer implemented method of generating a gradient boosting decision tree for obtaining predictions includes finding split points by sorting variable values of a feature by their gradient during training of the gradient boosting decision tree, performing a linear search to find a subset of variables with maximum split gain, and modifying a node of the gradient boosting decision tree to have multiple split points on the node for a feature as a function of the linear search. In a further example, a computer implemented method of controlling overfitting in a gradient boosting decision tree includes combining values of low population feature values into a virtual bin, fanning out the virtual bin into feature values having a low population, and including the low population feature values into multiple split points on a node of the gradient boosting decision tree.

Abstract: Methods, systems, and computer products are herein provided for lazy evaluation of input data by a machine learning (ML) framework. An ML pipeline receives input data and compiles a chain of operators into a chain of dataviews configured for lazy evaluation of the input data. Each dataview in the chain represents a computation over data as a non-materialized view of the data. The ML pipeline receives a request for column data and selects a chain of delegates comprising one or more delegates for one or more dataviews in the chain to fulfill the request. The ML pipeline processes the input data with the selected chain of delegates. The ML pipeline performs delegate chaining on a dataview. A feature value for a feature column of the dataview is determined based on the delegate chaining and provided to an ML algorithm to predict column data.

Abstract: Embodiments provide a machine learning framework that enables developers to author and deploy machine learning pipelines into their applications regardless of the programming language in which the applications are structured. The framework may provide a programming language-specific API that enables the application to call a plurality of operators provided by the framework. The framework provides any number of APIs, each for a different programming language. The pipeline generated via the application is represented as an execution graph comprising node(s), where each node represents a particular operator. When a pipeline is submitted for execution, calls to the operators are detected, and nodes corresponding to the operators are generated for the execution graph.

Abstract: An efficient, streaming-based, lazily-evaluated machine learning (ML) framework is provided. An ML pipeline of operators produce and consume a chain of dataviews representing a computation over data. Non-materialized (e.g., virtual) views of data in dataviews permit efficient, lazy evaluation of data on demand regardless of size (e.g., in excess of main memory). Data may be materialized by DataView cursors (e.g., movable windows over rows of an input dataset or DataView). Computation and data movement may be limited to rows for active columns without processing or materializing unnecessary data. A chain of dataviews may comprise a chain of delegates that reference a chain of functions. Assembled pipelines of schematized compositions of operators may be validated and optimized with efficient execution plans. A compiled chain of functions may be optimized and executed in a single call. Dataview based ML pipelines may be developed, trained, evaluated and integrated into applications.

Abstract: Embodiments provide a machine learning framework that enables developers to author and deploy machine learning pipelines into their applications regardless of the programming language in which the applications are structured. The framework may provide a programming language-specific API that enables the application to call a plurality of operators provided by the framework. The framework provides any number of APIs, each for a different programming language. The pipeline generated via the application is represented as an execution graph comprising node(s), where each node represents a particular operator. When a pipeline is submitted for execution, calls to the operators are detected, and nodes corresponding to the operators are generated for the execution graph.

Abstract: A computer implemented method of generating a gradient boosting decision tree for obtaining predictions includes finding split points by sorting variable values of a feature by their gradient during training of the gradient boosting decision tree, performing a linear search to find a subset of variables with maximum split gain, and modifying a node of the gradient boosting decision tree to have multiple split points on the node for a feature as a function of the linear search. In a further example, a computer implemented method of controlling overfitting in a gradient boosting decision tree includes combining values of low population feature values into a virtual bin, fanning out the virtual bin into feature values having a low population, and including the low population feature values into multiple split points on a node of the gradient boosting decision tree.

Abstract: Various methods and systems for implementing an availability management system for implementing an availability management, in distributed computing systems, are provided. An availability management system implements an availability manager and an availability configuration interface to meet availability guarantees for tenant infrastructure. The availability management systems operates with availability zones, computing clusters, fault and upgrade domains to allocate and de-allocate virtual machine sets of virtual machine instances to a distributed computing system based on tenant-defined availability parameters. The availability configuration interface of the availability management system supports receiving availability parameters that are used to generate an availability profile.

Abstract: Various methods and systems for implementing an availability management system for implementing an availability management, in distributed computing systems, are provided. An availability management system implements an availability manager and an availability configuration interface to meet availability guarantees for tenant infrastructure. The availability management systems operates with availability zones, computing clusters, fault and upgrade domains to allocate and de-allocate virtual machine sets of virtual machine instances to a distributed computing system based on tenant-defined availability parameters. The availability parameters are used to generate an availability profile. The availability manager is configured to, based on an availability profile, allocate the virtual machine sets based an allocation scheme. The availability manager specifically performs scaling-out, scaling-in and rebalancing operations for allocating and de-allocating the virtual machine sets.

Abstract: Various methods and systems for implementing an availability management system for implementing an availability management, in distributed computing systems, are provided. An availability management system implements an availability manager and an availability configuration interface to meet availability guarantees for tenant infrastructure. The availability management systems operates with availability zones, computing clusters, fault and upgrade domains to allocate and de-allocate virtual machine sets of virtual machine instances to a distributed computing system based on tenant-defined availability parameters. The availability manager is configured to: based on an availability profile, allocate the virtual machine sets across the availability zones using an allocation scheme.