Abstract: A method of using a computing device to determine a window size in variate time series data that includes receiving, by a computing device, variate time series data associated with a machine learning model. The computing device sets a moving window size and a standard deviation for the variate time series data. The computing device further calculates a moving window average for the variate time series data. The computing device additionally calculates a standard deviation across all variate time series data. The computing device sorts the standard deviations calculated in descending order. The computing device further iterates indices for the standard deviations until the indices have been visited by at least one anchor. The computing device iteratively expands each anchor to cover neighbors' anchors which have been visited by previous anchors. The computing device determines a window size based upon the expanded anchors.

Abstract: A method of using a computing device to train a neural network to recognize features in variate time series data that includes receiving, by a computing device, variate time series data. The computing device further receives results associated with the variate time series data. The computing device determines an anchor of the variate time series data. The computing device additionally determines one or more portions of the variate time series data which lead to a positive result. The computing device further determines one or more portions of the variate time series data which lead to a negative result. The computing device trains a neural network to interpret results of future variate time series data based upon the anchor, the one or more portions of the variate time series data which lead to the positive result, and the one or more portions of the variate time series data which lead to the negative result.

Abstract: A system and method for assessing Pay-As-You-Go (PAYG) Automatic machine learned (AutoML) model pipeline charge to a user on the basis of performance improvement achieved by configuring a model pipeline with performance enhancements relative to a performance obtained by a base model pipeline. The method performs a ranking of pipelines (customized models) based on a user-specified metric (for example, prediction accuracy, run time, F1 score) or combination of metrics. The price for ranked pipelines is specified based on a “surrogate” model where the surrogate model is fit to the base model price and the maximum price for a model. The base model price relates to use of a current cloud resource utilization-based pricing model. The pricing per model pipeline increments on the basis of performance metric(s) in a linear fashion, e.g., using a linear pricing model, or in an exponential fashion, e.g., using a fixed percentage hike price model.

Abstract: Mechanism are provided to select a machine learning model from an analytics model library based on ingested data. One or more pieces of clarified data are fused to provide time-correlated data tuples of data streams. One or more features are extracted from the time-correlated data tuples and scored based on a set of predetermined rules thereby generating discriminative scoring of trigger data. Utilizing the discriminative scoring of the trigger data, trigger data of a current analytics model being utilized by the data processing and one or more new analytics models from the analytics model library are scored. Responsive to the scoring of the trigger data indicating a selection of a different analytics model from the analytics model library, the current analytics model is replaced with a selected analytics model from the analytics model library such that the data processing system executes the selected analytics model.

Abstract: A system and method for automatically generating deep neural network architectures for time series prediction. The system includes a processor for: receiving a prediction context associated with a current use case; based on the associated prediction context, selecting a prediction model network configured for a current use case time series prediction task; replicating the selected prediction model network to create a plurality of candidate prediction model networks; inputting a time series data to each of the plurality of the candidate prediction model network; train, in parallel, each respective candidate prediction model network of the plurality with the input time series data; modifying each of the plurality of the candidate prediction model network by applying a respective different set of one or more model parameters while being trained in parallel; and determine a fittest modified prediction model network for solving the current use case time series prediction task.

Abstract: In an approach to unsupervised feature learning for relational data, a computer trains one or more entity aware autoencoders on one or more tables in a relational database, where each of the one or more entity aware autoencoders corresponds to one of the one or more tables in the relational database, and where each of the one or more entity aware autoencoders are comprised of an encoder and a decoder. A computer transforms each of the one or more tables in the relational database with the encoder of the corresponding trained entity aware autoencoder. A computer joins a first transformed table of the one or more tables in the relational database with each remaining one or more transformed tables in the relational database to form one or more joined tables. A computer aggregates the one or more joined tables. A computer outputs one or more feature representations.

Abstract: A method, a structure, and a computer system for predicting pipeline training requirements. The exemplary embodiments may include receiving one or more worker node features from one or more worker nodes, extracting one or more pipeline features from one or more pipelines to be trained, and extracting one or more dataset features from one or more datasets used to train the one or more pipelines. The exemplary embodiments may further include predicting an amount of one or more resources required for each of the one or more worker nodes to train the one or more pipelines using the one or more datasets based on one or more models that correlate the one or more worker node features, one or more pipeline features, and one or more dataset features with the one or more resources. Lastly, the exemplary embodiments may include identifying a worker node requiring a least amount of the one or more resources of the one or more worker nodes for training the one or more pipelines.

Abstract: Split an input dataset into training and test datasets; the former includes a plurality of data examples, each represented as a feature vector, and having an associated true label. Split the training dataset into a plurality of training data subsets; for each, train a corresponding machine learning model to obtain a plurality of such models, and apply same to the test dataset to obtain a plurality of predicted labels and prediction scores. For each of the plurality of examples, compute an agreement metric based on a corresponding one of the associated true labels; corresponding ones of the predicted labels; and corresponding ones of the prediction scores. Based on the computed metric, select, for at least some of the true label values, appropriate ones of the data examples to be added to a regression set. Add the appropriate ones of the data examples from the test dataset to the regression set.

Abstract: A method, computer program product, and/or computer system improves a future efficiency of a specific system. One or more processors receive multiple historical data snapshots that describe past operational states of a specific system. The processor(s) identify a time series pattern for the time series of data in the multiple historical snapshots and calculate their variability. The processor(s) then determine that the variability in a first sub-set of the time series pattern is larger than a predefined value, and determine that future values of the first set of the time series pattern are a set of non-forecastable future values. The processor(s) also determine that the variability in a second sub-set of the time series pattern for the data is smaller than the predefined value, and utilizes this second sub-set to modify the specific system at a current time.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate automated model pipeline generation with entity monitoring, interaction, and/or intervention are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that provides a recommended input action corresponding to a model pipeline candidate being evaluated in an automated model pipeline generation process. The computer executable components can further comprise a visualization render component that renders an input visualization corresponding to the model pipeline candidate based on the recommended input action.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.

Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.

Abstract: A mechanism is provided to identify wall-clock time reference dependency in one or more software components of a data analytics solution. The data analytics solution is decomposed into a set of software components. A first software component of the set of software components is deployed to a first computer server and the remaining software components are deployed to a second computer server. A system clock time on the first computer server is changed to differ from the system clock of the second computer server. Based on executing a test on the data analytics solution, a determination is made of whether the first software component, is wall-clock time independent. Responsive to the test of the of the software component failing indicating that the wall-clock time of the software component is dependent of the system clock time difference, the software component is recorded as wall-clock time dependent and an administrator is notified.

Abstract: Mechanism are provided to select a machine learning model from an analytics model library based on ingested data. One or more pieces of clarified data are fused to provide time-correlated data tuples of data streams. One or more features are extracted from the time-correlated data tuples and scored based on a set of predetermined rules thereby generating discriminative scoring of trigger data. Utilizing the discriminative scoring of the trigger data, trigger data of a current analytics model being utilized by the data processing and one or more new analytics models from the analytics model library are scored. Responsive to the scoring of the trigger data indicating a selection of a different analytics model from the analytics model library, the current analytics model is replaced with a selected analytics model from the analytics model library such that the data processing system executes the selected analytics model.

Abstract: For a plurality of hosts, observe first time-varying characteristics including network throughput, central processing unit (CPU) usage, and/or memory usage; second time-varying characteristics including software configuration; and time-invariant characteristics including hardware configuration, at a plurality of timestamps. Construct a restricted HMM configured to predict actual host states, wherein the first time-varying characteristics include observed variables. The current observed variables depend on current values of the hidden variables and prior timestamp distribution of the observed variables. The former in turn depend on prior timestamp values of the hidden variables, the time-invariant characteristics of the hosts. and current timestamp values of the second time-varying characteristics.

Abstract: A mechanism is provided for implementing a model update mechanism to update new models in real time while avoiding data loss and system downtime. Responsive to receiving a request to update a scorer model currently being executed by an existing worker thread in the data processing system, the model update mechanism initializing a new worker thread. The model update mechanism loads an updated scorer model into the new worker thread and initializes a state transfer from the existing worker thread to the new worker thread. The model update mechanism executes the updated scorer model such that the updated scorer model scores the input data. The model update mechanism then outputs a prediction based on the updated scorer model processing of the input data.

Abstract: Split an input dataset into training and test datasets; the former includes a plurality of data examples, each represented as a feature vector, and having an associated true label. Split the training dataset into a plurality of training data subsets; for each, train a corresponding machine learning model to obtain a plurality of such models, and apply same to the test dataset to obtain a plurality of predicted labels and prediction scores. For each of the plurality of examples, compute an agreement metric based on a corresponding one of the associated true labels; corresponding ones of the predicted labels; and corresponding ones of the prediction scores. Based on the computed metric, select, for at least some of the true label values, appropriate ones of the data examples to be added to a regression set. Add the appropriate ones of the data examples from the test dataset to the regression set.

Abstract: A mechanism is provided to identify wall-clock time reference dependency in one or more software components of a data analytics solution. The data analytics solution is decomposed into a set of software components. A first software component of the set of software components is deployed to a first computer server and the remaining software components are deployed to a second computer server. A system clock time on the first computer server is changed to differ from the system clock of the second computer server. Based on executing a test on the data analytics solution, a determination is made of whether the first software component, is wall-clock time independent. Responsive to the test of the of the software component failing indicating that the wall-clock time of the software component is dependent of the system clock time difference, the software component is recorded as wall-clock time dependent and an administrator is notified.

Abstract: A vehicle splash guard includes a flexible body and a body panel attachment area. The body panel attachment area is integrated with the flexible body, the body panel attachment area is configured to abut a vehicle body panel component, the body panel attachment area including a pair of positioning tabs extending towards the vehicle body panel component and receiving the vehicle body panel component therebetween in a state in which the splash guard is installed to the vehicle body panel component.

Abstract: A vehicle splash guard includes a flexible body and a body panel attachment area. The body panel attachment area is integrated with the flexible body, the body panel attachment area is configured to abut a vehicle body panel component, the body panel attachment area including a pair of positioning tabs extending towards the vehicle body panel component and receiving the vehicle body panel component therebetween in a state in which the splash guard is installed to the vehicle body panel component.