Abstract: A method includes receiving a point data anomaly detection query from a user. The query requests the data processing hardware to determine a quantity of anomalous point data values in a set of point data values. The method includes training a model using the set of point data values. For at least one respective point data value in the set of point data values, the method includes determining, using the trained model, a variance value for the respective point data value and determining that the variance value satisfies a threshold value. Based on the variance value satisfying the threshold value, the method includes determining that the respective point data value is an anomalous point data value. The method includes reporting the determined anomalous point data value to the user.

Abstract: A method includes receiving a time series anomaly detection query from a user and training one or more models using a set of time series data values. For each respective time series data value in the set, the method includes determining, using the trained models, an expected data value for the respective time series data value and determining a difference between the expected data value and the respective time series data value. The method also includes determining that the difference between the expected data value and the respective time series data value satisfies a threshold. In response to determining that the difference between the expected data value and the respective time series data value satisfies the threshold, the method includes determining that the respective time series data value is anomalous and reporting the anomalous respective time series data value to the user.

Abstract: A method, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving, from a user device, a hyperparameter optimization request requesting optimization of one or more hyperparameters of a machine learning model. The operations include obtaining training data for training the machine learning model and determining a set of hyperparameter permutations of the one or more hyperparameters. For each respective hyperparameter permutation in the set of hyperparameter permutations, the operations include training a unique machine learning model using the training data and the respective hyperparameter permutation and determining a performance of the trained model. The operations include selecting, based on the performance of each of the trained unique machine learning models of the user device, one of the trained unique machine learning models.

Abstract: A data processing system is provided having a processor and analysing circuitry for identifying a SIMD instruction associated with a first SIMD instruction set and replacing it by a functionally-equivalent scalar representation and marking that functionally-equivalent scalar representation. The marked functionally-equivalent scalar representation is dynamically translated using translation circuitry upon execution of the program to generate one or more corresponding translated instructions corresponding to a instruction set architecture different from the first SIMD architecture corresponding to the identified SIMD instruction.

Abstract: A data processing system is provided having a processor and analysing circuitry for identifying a SIMD instruction associated with a first SIMD instruction set and replacing it by a functionally-equivalent scalar representation and marking that functionally-equivalent scalar representation. The marked functionally-equivalent scalar representation is dynamically translated using translation circuitry upon execution of the program to generate one or more corresponding translated instructions corresponding to a instruction set architecture different from the first SIMD architecture corresponding to the identified SIMD instruction.