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    Train ML Models With SQL You Already Know

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  • 1
    Synthetic Data Kit

    Synthetic Data Kit

    Tool for generating high quality Synthetic datasets

    ...It supports generation of rationales/chain-of-thought variants, configurable sampling, and guardrails so outputs meet format constraints and quality checks. Examples and guides show how to target task-specific behaviors like tool use or step-by-step reasoning, then save directly into training-ready files.
    Downloads: 0 This Week
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  • 2
    Zylthra

    Zylthra

    Zylthra: A PyQt6 app to generate synthetic datasets with DataLLM.

    Welcome to Zylthra, a powerful Python-based desktop application built with PyQt6, designed to generate synthetic datasets using the DataLLM API from data.mostly.ai. This tool allows users to create custom datasets by defining columns, configuring generation parameters, and saving setups for reuse, all within a sleek, dark-themed interface.
    Downloads: 0 This Week
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  • 3
    Synth

    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.
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  • 4
    DBFeeder

    DBFeeder

    Highly Customizable Test Data Generator

    DBFeeder is a great tool to generate synthetic testdata for Oracle Databases and it is ideal for companies who wants to outsource development. Thanks to his original approach, data can be highly customizable and it even fits primary and foreign keys constraints of tables.
    Downloads: 0 This Week
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    Host LLMs in Production With On-Demand GPUs

    NVIDIA L4 GPUs. 5-second cold starts. Scale to zero when idle.

    Deploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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  • 5
    Tofu

    Tofu

    Tofu is a Python tool for generating synthetic UK Biobank data

    Tofu is a Python library for generating synthetic UK Biobank data. The UK Biobank is a large open-access prospective research cohort study of 500,000 middle-aged participants recruited in England, Scotland and Wales. The study has collected and continues to collect extensive phenotypic and genotypic detail about its participants, including data from questionnaires, physical measures, sample assays, accelerometry, multimodal imaging, genome-wide genotyping and longitudinal follow-up for a...
    Downloads: 0 This Week
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  • 6

    OraMasking

    Data masking tool for Oracle database

    For Oracle database, mask sensitive data by replacement (static or expression), substitution (synthetic data set is included), random values (Lorum Ipsum) or deletion. Generates update/delete statements or triggers or runs directly against the database.
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  • 7
    Ava: Testdata Xsl

    Ava: Testdata Xsl

    generates Testdata on base of excel: creates xml,excel,csv,html,sql,+

    this tool for test-data-generation receives an 'excel-sheet' as primary input. second important paramter is the 'number of test-records to produce'. The excel-data will be reused as long data is needed. This tool is hightly paramatrisazable by the use of 'xsl scripts'. data can be created, updated, modified and finally exported in a format of your choice Main Fuctions: (1) Generates Testdata (excel, xsl, xml) (2) Exports generated testdata in multiple formats (csv, excel, html, sql-insert, individual by xsl extension) (3) Collect all processed data in excel-files (4) plus: Xsl Executor, which let's you run xsl-scripts independently (5) plus: User Interface
    Downloads: 0 This Week
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  • 8

    A Data Generator

    A tool to generate synthetic test data useful to Record matchers

    With growing amount of information from multiple sources it has become very hard to relate information to the correct real life entities. Record matching software try to solve this by machine learning techniques. To do this effectively, its necessary to train the record matcher with proper test data which is identical to real life data. Hence, there is a need for a data generator to create the synthetic data to be used for evaluating the quality and capability of record matching software....
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