Abstract: A storage system controller of a storage system receives a dataset stored in the storage system and executes an analytics application that ingests the dataset from the storage system. The storage system controller generates results from execution of the analytics application.

Abstract: Scaling storage resources in a storage volume, including: monitoring a usage of a volume in a storage pool that includes one or more cloud-based storage systems; determining that the usage of the volume exceeds a threshold usage; and based on the determination, expanding the resources that are included in the storage pool for servicing the volume, including: instantiating one or more new virtual drives that are included in the one or more cloud-based storage systems; and adding the one or more new virtual drives to the storage pool.

Abstract: A direct data path between a storage system and memory of one or more processing devices is enabled for a machine learning workload to be executed on the processing devices. Remote direct memory access (RDMA) is configured to transfer data between the storage system and the memory of the one or more processing devices while bypassing host memory. Data transfers for the machine learning workload are executed over the direct data path.

Abstract: Log data generated by a computing system is received. An analytics operation is executed on the log data. Pattern recognition associated with the log data is applied to identify anomalous behaviors of the computing system.

Abstract: Executing a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: receiving, by a graphical processing unit (‘GPU’) server, a dataset transformed by a storage system that is external to the GPU server; and executing, by the GPU server, one or more machine learning algorithms using the transformed dataset as input.

Abstract: Execution of a pipeline of a client system and access patterns to one or more storage resources used by the pipeline is monitored. A bottleneck in the pipeline is identified based at least in part on the access patterns. A reconfiguration of resources is initiated, including reallocating compute resources or storage resources, to resolve the bottleneck in the pipeline.

Abstract: A dataset is received from a data source. A real-time analytics operation is executed on at least a portion of the dataset prior to fully persisting the dataset. Results of the real-time analytics operation are stored.

Abstract: A method is disclosed for managing transformed datasets in a compute cluster environment. The method includes identifying, based on one or more machine learning models to be executed on a compute cluster comprising a plurality of GPU servers, one or more transformations to apply to a dataset. The method further includes generating a transformed dataset based on the one or more transformations, storing the transformed dataset, receiving a request to transmit the transformed dataset to at least one GPU server of the plurality of GPU servers, and, responsive to the request, transmitting the stored transformed dataset to the at least one GPU server without re-performing the one or more transformations on the dataset.

Abstract: Scaling storage resources in a storage volume, including: monitoring a usage of a volume in a storage pool that includes one or more cloud-based storage systems; determining that the usage of the volume exceeds a threshold usage; and based on the determination, expanding the resources that are included in the storage pool for servicing the volume, including: instantiating one or more new virtual drives that are included in the one or more cloud-based storage systems; and adding the one or more new virtual drives to the storage pool.

Abstract: Executing a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: receiving, by a graphical processing unit (‘GPU’) server, a dataset transformed by a storage system that is external to the GPU server; and executing, by the GPU server, one or more machine learning algorithms using the transformed dataset as input.

Abstract: Monitoring and configuring storage systems using generative AI, including: receiving, from a user, a request associated with a storage system directed to a generative artificial intelligence (AI) model; accessing, in response to the request, telemetry data generated by the storage system; and generating, using the generative AI model and based on the telemetry data, a response to the request

Abstract: Configurable automated redaction of log data, including: selecting, based on one or more configurable rules, one or more portions of log data; generating obfuscated log data by replacing the one or more portions of log data with one or more obfuscated values; presenting the obfuscated log data; and providing, in response to receiving an approval of the obfuscated log data, the obfuscated log data to a remotely disposed computing device.

Abstract: Controlling the direction of replication between cross-region replicated object stores is disclosed.

Abstract: Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: storing, within one or more storage systems, a transformed dataset generated by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers; and transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.

Abstract: Improving machine learning models in an artificial intelligence infrastructure includes: storing, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset; and storing, within the one or more storage systems, information describing only portions of previous versions of a machine learning model that differ from a current version of the machine learning model, wherein the previous versions used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure.

Abstract: Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: storing, within one or more storage systems, a transformed dataset generated by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers; and transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.

Abstract: Providing application-side infrastructure to control cross-region replicated object stores is disclosed. An embodiment includes determining, for an object store replicated across a plurality of storage systems at a plurality of locations, a minimum time interval between requests to modify the object store that are received by differing storage systems of the plurality of storage systems that ensures a predictable result on all storage systems among which the requested modifications are replicated; and providing, through an application programming interface (API) to a client that utilizes the object store, one or more object store parameters including the minimum time interval.

Abstract: Improving machine learning models in an artificial intelligence infrastructure includes: storing, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset; and storing, within the one or more storage systems, information describing only portions of previous versions of a machine learning model that differ from a current version of the machine learning model, wherein the previous versions used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure.

Abstract: Creating a containerized data analytics pipeline, including: creating a data analytics pipeline having a component that is deployed within a container that executes in a container cluster that is external to shared storage accessible by components of the data analytics pipeline; and responsive to detecting that a component of the data analytics pipeline has failed, deploying another instance of the component in a failover container that is configured to support one or more components of the data analytics pipeline.

Abstract: A method of data transfer between storage systems includes determining to transfer a dataset stored at a first storage system to a second storage system and invoking an operation to retrieve, for portions of the dataset having a size less than a threshold size, an identifier, metadata, and data stored in the portions of the dataset. The method further includes transmitting the identifiers, the metadata, and the data for the portions of the dataset having a size less than the threshold size to the second storage system.