Abstract: Techniques and architectures for data modeling and management. Data modeling services are provided to agents within multiple different operating environments of a computing environment having at least one database stored on one or more physical memory devices communicatively coupled with one or more hardware processors the one or physical memory devices. Building and versioning of data modeling projects is coordinated and data utilized for the data modeling projects with the one or more hardware processors.

Abstract: Methods, systems, and devices for multi-tenant workflow processing are described. In some cases, a cloud platform may utilize a set of pre-defined batch processes (e.g., workflow templates) and tenant-specific configurations for instantiating and executing tenant-specific batch processes for each tenant of a user. As such, the cloud platform may utilize common data process workflows for each tenant, where a configuration specifies tenant-specific information for the common data process workflows. The workflow templates may include a set of job definitions (e.g., actions for a server to execute) and a schedule defining the frequency for running the templates for a specific project. The configurations may indicate a tenant to execute the workflow templates for, and may include tenant-specific information to override default template information.

Abstract: Techniques and architectures for data modeling and management. Data modeling services are provided to agents within multiple different operating environments of a computing environment having at least one database stored on one or more physical memory devices communicatively coupled with one or more hardware processors the one or physical memory devices. Building and versioning of data modeling projects is coordinated and data utilized for the data modeling projects with the one or more hardware processors.

Abstract: Techniques and architectures for data modeling and management. Data modeling services are provided to agents within multiple different operating environments of a computing environment having at least one database stored on one or more physical memory devices communicatively coupled with one or more hardware processors the one or physical memory devices. Building and versioning of data modeling projects is coordinated and data utilized for the data modeling projects with the one or more hardware processors.

Abstract: Methods, systems, and devices for multi-tenant workflow processing are described. In some cases, a cloud platform may utilize a set of pre-defined batch processes (e.g., workflow templates) and tenant-specific configurations for instantiating and executing tenant-specific batch processes for each tenant of a user. As such, the cloud platform may utilize common data process workflows for each tenant, where a configuration specifies tenant-specific information for the common data process workflows. The workflow templates may include a set of job definitions (e.g., actions for a server to execute) and a schedule defining the frequency for running the templates for a specific project. The configurations may indicate a tenant to execute the workflow templates for, and may include tenant-specific information to override default template information.

Abstract: In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing predictive engine evaluation and replay of engine performance.

Abstract: Techniques and architectures for data modeling and management. Data modeling services are provided to agents within multiple different operating environments of a computing environment having at least one database stored on one or more physical memory devices communicatively coupled with one or more hardware processors the one or physical memory devices. Building and versioning of data modeling projects is coordinated and data utilized for the data modeling projects with the one or more hardware processors.

Abstract: In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing predictive engine evaluation and replay of engine performance.

Abstract: Disclosed are methods and systems of tracking the deployment of a predictive engine for machine learning, including steps to deploy an engine variant of the predictive engine based on an engine parameter set, wherein the engine parameter set identifies at least one data source and at least one algorithm; receive one or more queries to the deployed engine variant from one or more end-user devices, and in response, generate predicted results; receive one or more actual results corresponding to the predicted results; associate the queries, the predicted results, and the actual results with a replay tag, and record them with the corresponding deployed engine variant.

Abstract: Disclosed are methods and systems for creating, evaluating, and tuning a predictive engine for machine learning, and for replaying performance of the predictive engine, including steps to deploy a variant of the predictive engine; receive a plurality of queries to the predictive engine variant from an end-user device; apply a query segment filter to the plurality of queries to generate a replay group of queries, wherein for each query in the replay group, the engine variant generates a corresponding predicted result, receives a corresponding actual result, and computes a corresponding prediction score; group queries in the replay group into one or more segments; and generate, as a replay of the performance of the engine variant, an accumulated prediction score over each of the one or more query segments of the replay group by applying an accumulation function. The present invention substantially improves on systems that utilize predictive engines.

Abstract: Disclosed are methods and systems of creating, evaluating, and tuning a predictive engine for machine learning, including steps to deploy the predictive engine with an initial parameter set; receive queries to the deployed engine variant and in response, generate predicted results; receive corresponding actual results; associate the queries, the predicted results, and the actual results with a replay tag; evaluate the performance of the deployed engine variant; generate a new engine parameter set based on tuning of one or more parameters of the initial engine parameter set, according to the evaluation results; deploy the new engine variant to replace the initial engine variant; receive a replay request from an operator specifying the currently or a previously deployed engine variant; and in response to the replay request, replay at least one of the queries, the corresponding predicted results, the actual results, and the evaluation results.

Abstract: Accessing data at a memory is described. A request associated with a read or write operation is received, wherein the request includes a logical address associated with the memory. A physical address is generated based at least in part on the logical address. A block of data at the memory that includes data associated with the physical address is determined. Data at the determined block of data and a corresponding set of ECC from the memory are accessed. Whether the accessed data can be decoded based at least in part on the corresponding set of ECC is determined.

Abstract: Accessing data at a memory is described. A request associated with a read or write operation is received, wherein the request includes a logical address associated with the memory. A physical address is generated based at least in part on the logical address. A block of data at the memory that includes data associated with the physical address is determined. Data at the determined block of data and a corresponding set of ECC from the memory are accessed. Whether the accessed data can be decoded based at least in part on the corresponding set of ECC is determined.

Abstract: Accessing data at a memory is described. A request associated with a read or write operation is received, wherein the request includes a logical address associated with the memory. A physical address is generated based at least in part on the logical address. A block of data at the memory that includes data associated with the physical address is determined. Data at the determined block of data and a corresponding set of ECC from the memory are accessed. Whether the accessed data can be decoded based at least in part on the corresponding set of ECC is determined.