Abstract: An example computing platform is configured to (i) cause a client device to display an interface for deploying a new data science environment, where the interface presents (a) a list of data science applications and (b) a set of user-defined configuration parameters, (ii) receive, from the client device, data indicating (a) a user selection of a given data science application from the list and (b) a user selection of one or more user-defined configuration parameters from the set, (iii) based on the user selection of the given data science application, determine a deployment template for use in deploying the new data science environment, the deployment template specifying (a) an executable environment package and (b) a set of predefined configuration parameters, and (iv) use the given executable environment package, the set of predefined configuration parameters, and the one or more user-defined configuration parameters to deploy the new data science environment.

Abstract: A computing platform may be installed with software technology for creating and executing user-defined anomaly detection rules that configures the computing platform to: (1) receive, from a client device, data defining a given anomaly detection rule that has been created by a user, wherein the given anomaly detection rule comprises at least one anomaly condition that is to be applied to at least one streaming event queue, (2) store a data representation of the given anomaly detection rule in a data store, (3) convert the data representation of the given anomaly detection rule to a streaming query statement, (4) iteratively apply the streaming query statement to the at least one streaming event queue, and (5) while iteratively applying the streaming query statement, make at least one determination that the at least one anomaly condition is satisfied and then cause at least one anomaly notification to be issued to the user.

Abstract: A computing platform may be installed with software technology for creating and executing user-defined anomaly detection rules that configures the computing platform to: (1) receive, from a client device, data defining a given anomaly detection rule that has been created by a user, wherein the given anomaly detection rule comprises at least one anomaly condition that is to be applied to at least one streaming event queue, (2) store a data representation of the given anomaly detection rule in a data store, (3) convert the data representation of the given anomaly detection rule to a streaming query statement, (4) iteratively apply the streaming query statement to the at least one streaming event queue, and (5) while iteratively applying the streaming query statement, make at least one determination that the at least one anomaly condition is satisfied and then cause at least one anomaly notification to be issued to the user.

Abstract: An example computing platform is configured to (i) cause a client device to display an interface for deploying a new data science environment, where the interface presents (a) a list of data science applications and (b) a set of user-defined configuration parameters, (ii) receive, from the client device, data indicating (a) a user selection of a given data science application from the list and (b) a user selection of one or more user-defined configuration parameters from the set, (iii) based on the user selection of the given data science application, determine a deployment template for use in deploying the new data science environment, the deployment template specifying (a) an executable environment package and (b) a set of predefined configuration parameters, and (iv) use the given executable environment package, the set of predefined configuration parameters, and the one or more user-defined configuration parameters to deploy the new data science environment.