Abstract: A method, computer program product, and computing system for processing a query using a generative artificial intelligence (AI) model. A topic of the query is extracted. A weighting for the topic of the query is generated. A weighted query topic embedding for the topic of the query is generated. A candidate chunk is identified from a plurality of chunks of a target document by determining a similarity between the weighted query topic embedding and a plurality of chunk embeddings for the plurality of chunks. A prompt is generated using the query and the candidate chunk. The prompt is provided to the generative AI model.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document. A plurality of chunk summaries are generated by generating a summary for each respective chunk of the plurality of chunks. A plurality of chunk summary embeddings are generated by generating an embedding of the summary for each respective chunk. The plurality of chunk summary embeddings are provided for processing a query using the generative AI model.

Abstract: A method, computer program product, and computing system for forecasting a temperature of a storage object of a storage system using a first machine learning model and a plurality of input/output (IO) features. The storage object may be divided into a plurality of storage sub-objects. A temperature may be determined for each storage sub-object with a subset of the plurality of IO features using a second machine learning model. A portion of the temperature of the storage object may be projected onto the temperature of each of the plurality of storage sub-objects based upon, at least in part, the temperature determined for each storage sub-object and the temperature determined for each storage object.

Abstract: A method, computer program product, and computing system for receiving a telemetry data stream associated with a storage system. An erroneous portion of the telemetry data stream is identified by processing the telemetry data stream using an error detection process. The erroneous portion of the telemetry data stream is filtered by removing the erroneous portion of the telemetry data stream from the telemetry data stream.

Abstract: A method, computer program product, and computing system for forecasting a temperature of a storage object of a storage system using a machine learning model. The storage object may be divided into a plurality of storage sub-objects. A temperature may be determined for each storage sub-object using a simple moving average. A portion of the temperature of the storage object may be projected onto the temperature of each of the plurality of storage sub-objects based upon, at least in part, the temperature determined for each storage sub-object and the temperature determined for each storage object.

Abstract: An apparatus comprises at least one processing device configured to determine a machine learning model type to be deployed on a target computing device, to identify machine learning model performance metrics for operating the determined machine learning model type on the target computing device, and to determine whether any available instances of the determined machine learning model type (i) have hardware requirements compatible with a hardware configuration of the target computing device and (ii) meet the identified machine learning model performance metrics. The at least one processing device is also configured, responsive to determining that at least a subset of the available instances meet (i) and (ii), to select a given machine learning model instance of the determined machine learning model type from the subset of the available instances of the determined machine learning model type, and to deploy the given machine learning model instance to the target computing device.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document. A plurality of chunk embeddings are generated from the plurality of chunks. A query is processed using a generative artificial intelligence (AI) model. A query embedding is generated from the query. A plurality of candidate chunk embeddings are identified from the plurality of chunk embeddings based upon, at least in part, a chunk size and a chunk similarity score associated with each chunk and a performance metric associated with the query. A prompt is generated using the query embedding and the plurality of candidate chunk embeddings. The prompt is provided to the generative AI model.

Abstract: Techniques for providing a machine learning (ML)-based framework for detecting and troubleshooting network-related issues in large storage fabrics. The techniques include detecting, based on an output of an ML model, a network-related issue in a distributed storage infrastructure. The ML model operates on telemetry data obtained from network elements, and computing/storage nodes on a storage network. A multilayer representation of the storage network includes a physical layer, a logical layer, and a service layer. The techniques include obtaining a correlation between the network-related issue and an activity, service, or status of the network elements/nodes in two or more layers of the multilayer representation. The correlation identifies a context of the network-related issue with respect to the network elements/nodes in the two or more layers.

Abstract: A method, computer program product, and computing system for receiving a telemetry data stream associated with a storage system. An erroneous portion of the telemetry data stream is identified by processing the telemetry data stream using an error detection process. The erroneous portion of the telemetry data stream is filtered by removing the erroneous portion of the telemetry data stream from the telemetry data stream.

Abstract: An apparatus comprises at least one processing device configured to obtain a query comprising search text and a context identifying documents to be searched. The processing device is also configured to generate document chunks by parsing the documents, each document chunk comprising a portion of content of the documents, and to determine chunk boosting factors for the document chunks based on document formatting of textual elements within the document chunks. The processing device is further configured to select a subset of the document chunks based on determining a similarity between content of the document chunks and the search text using the determined chunk boosting factors, to generate a prompt for input to a machine learning system comprising the selected subset of the document chunks, to apply the prompt to the machine learning system, and to provide an answer to the query based on an output of the machine learning system.

Abstract: Techniques for providing a centralized framework for forecasting IT component failures. The techniques include collecting raw telemetry data specific to different IT component domains, and transforming the telemetry data into structured telemetry data. The techniques include performing feature engineering on the structured telemetry data to obtain features relevant to IT component failures in each IT component domain, and, for each IT component domain, using the features to generate a customized ML model. The techniques include accessing features relevant to IT component failures in each IT component domain, accessing a customized ML model for forecasting IT component failures in the IT component domain, and forecasting IT component failures in the IT component domain using the customized ML model.

Abstract: Techniques for detecting impactful performance anomalies in storage systems. The techniques include obtaining, for each performance metric of a storage system's workload, a training set of series diffs based on a threshold. Each diff represents a difference between an observed value from an observed set of time series values for the performance metric and a normalized value from a corresponding set of normalized time series values. The techniques include applying the training set of series diffs for each performance metric to an unsupervised anomaly detection algorithm and running the algorithm to identify potentially impactful anomalies in a multi-dimensional search space. The techniques include identifying impactful anomalies from among the potentially impactful anomalies that exceed an anomaly score.

Abstract: A method, computer program product, and computing system for processing a plurality of input/output (IO) requests on a storage object within a storage system datapath using a plurality of processing cores of a storage system. The plurality of IO requests are sampled from the plurality of processing cores. An IO feature matrix is generated using the periodically sampled IO requests. The IO feature matrix is provided to a machine learning model that is separate from the storage system datapath. A machine learning model inference result is generated by processing the IO feature matrix using the machine learning model. The machine learning model inference result is provided to the storage system datapath. A storage system policy is executed using the machine learning model inference result.

Abstract: Techniques for providing a centralized framework for forecasting IT component failures. The techniques include collecting raw telemetry data specific to different IT component domains, and transforming the telemetry data into structured telemetry data. The techniques include performing feature engineering on the structured telemetry data to obtain features relevant to IT component failures in each IT component domain, and, for each IT component domain, using the features to generate a customized ML model. The techniques include accessing features relevant to IT component failures in each IT component domain, accessing a customized ML model for forecasting IT component failures in the IT component domain, and forecasting IT component failures in the IT component domain using the customized ML model.

Abstract: A method, computer program product, and computing system for generating a respective diagnostic history associated with each physical storage device of a plurality of physical storage devices. A subset of diagnostic history associated with a class of failing physical storage devices is identified from the diagnostic history associated with each physical storage device. The subset of diagnostic history associated with the class of failing physical storage devices is enhanced. A physical storage device failure event for a target physical storage device is forecast using a machine learning model and the enhanced subset of diagnostic history associated with the class of failing physical storage devices.

Abstract: A method, computer program product, and computing system for processing a plurality of historical input/output (IO) requests associated with a storage object of a storage system. A plurality of IO features may be generated using the plurality of historical IO requests. An active data set for the storage object may be forecasted for a particular future time interval using a machine learning model based upon, at least in part, the plurality of IO features.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document, wherein the document includes the plurality of text portions and a plurality of images. Each chunk is indexed using a word embedding. Each of the plurality of images is indexed based upon, at least in part, a position of a respective image relative to a corresponding chunk. An image placeholder is generated for each of the plurality of images. A plurality of image-enhanced embeddings is generated by inserting the image placeholder for each of the plurality of images into a respective word embedding for the corresponding chunk. The plurality of image-enhanced embeddings are provided for processing a query using a generative artificial intelligence (AI) model.

Abstract: A method, computer program product, and computing system for identifying a plurality of headings from a document by processing a hierarchical structure associated with the document including the plurality of headings and a plurality of content portions within the plurality of headings. A plurality of respective chunks are generated using the plurality of headings and a prompt size limitation associated with a prompt of a generative artificial intelligence (AI) model. The plurality of respective chunks are provided for generating a prompt for the generative AI model.

Abstract: Architectures and techniques are described that can provide enhanced accessibility for in-store shoppers such as a shopper with a vision impairment. For example, the disclosed techniques can operate to dynamically generate a shopping list from freeform (e.g., speech) item descriptions. The shopping list derived from the item descriptions can include actual, specific product identifiers for products offered for sale at a physical store location. Furthermore, an associated in-store navigation route to products of the shopping list can be generated based on any one of several different collection techniques or approaches.

Abstract: A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document. A plurality of chunk embeddings are generated from the plurality of chunks. A query is processed using a generative artificial intelligence (AI) model. A query embedding is generated from the query. A plurality of candidate chunks are identified from the plurality of chunks based upon, at least in part, a similarity between the plurality of chunk embeddings and the query embedding. An amount non-overlapping content of each candidate chunk is determined relative to each other candidate chunk. A subset of the plurality of candidate chunks are selected for inclusion in a prompt with the query based upon, at least in part, the amount of non-overlapping content of each candidate chunk.