Search Results for "data capture framework"
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Showing 8 open source projects for "data capture framework"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent... -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. -
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Bogus
A simple and sane fake data generator for C#, F#, and VB.NET
Bogus is a simple and sane fake data generator for .NET languages like C#, F# and VB.NET. Bogus is fundamentally a C# port of faker.js and inspired by FluentValidation's syntax sugar. Bogus will help you load databases, UI and apps with fake data for your testing needs. When Bogus updates locales from faker.js or issues bug fixes, sometimes deterministic sequences can change. Changes to deterministic outputs are usually highlighted in the release notes. Changes to deterministic outputs is... -
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Synth
The Declarative Data Generator
Synth is an open-source data-as-code tool that provides a simple CLI workflow for generating consistent data in a scalable way. Use Synth to generate correct, anonymized data that looks and quacks like production. Generate test data fixtures for your development, testing, and continuous integration. Generate data that tells the story you want to tell. Specify constraints, relations, and all your semantics. Seed development and environments and CI. Anonymize sensitive production data. Create... -
Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms,... -
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benerator is a framework for creating realistic and valid high-volume test data, used for load and performance testing and showcase setup. Data is generated from an easily configurable metadata model and exported to databases, XML, CSV or flat files.
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DATA Gen™
DATA Gen™ - Test Data Generator to generate realistic test data.
DATA Gen™ Test Data Generator offers facilities to automate the task of creating test data for new or existing data bases. It helps lower the programming effort required, while reducing manual test data generation errors and the ripple effect that they cause on production systems, users and maintenance. -
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JRandO is a test data generator or better test object generator framework. It can be used in JUnit tests or in performance test (for e.g. using JMeter). It may also be useful in anonymization of data or in a simulation environment.
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