{
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        "author": "Layne Sadler",
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        "description": "<!-- \n\tThis page is formatted for GitHub's markdown renderer \n\tNote that GitHub does not allow for inline style or <script> tags.\n\n\tIt is also displayed on PyPI, which has slightly different formatting\n\te.g. can't use html <center> tags.\n-->\n<a href=\"https://badge.fury.io/py/aiqc\"><img src=\"https://badge.fury.io/py/aiqc.svg\" alt=\"PyPI version\" height=\"18\"></a>\n<a href=\"https://docs.aiqc.io\"> <img src=\"https://readthedocs.org/projects/aiqc/badge/?version=latest\" alt=\"docs status\" height=\"18\"></a>\n[![License](https://img.shields.io/badge/License-BSD_3--Clause-brightgreen.svg)](https://opensource.org/licenses/BSD-3-Clause)\n\n\n</br>\n<h1 align='center'>\ud83d\udcda&nbsp;&nbsp;<a href=\"https://docs.aiqc.io\">Documentation</a></h1>\n</br></br>\n\n<a href=\"https://docs.aiqc.io\">\n\t<img src=\"https://raw.githubusercontent.com/aiqc/aiqc/main/docs/_static/images/web/framework_nov24.png\" width=\"95%\" alt=\"framework\"/>\n</a>\n</br></br></br>\n\n\n<h2>Technical Overview</h2>\n</br>\n<h3>What is it?</h3>\n<p>\n\tAIQC is an open source Python package that provides a <i>declarative API for end-to-end MLOps</i> (dataset registration, preprocessing, experiment tracking, model evaluation, inference, post-processing, etc) in order to make deep learning more accessible to researchers.\n</p>\n</br>\n<h3>How does it work?</h3>\n<p>\n\tThe backend is a <i>SQLite object-relational model (ORM)</i> for machine learning objects (Dataset, Feature, Label, Splits, Algorithm, Job, etc). The high-level API stacks these building blocks into <i>standardized workflows</i> for various: analyses (classify, regress, generate), data types (tabular, sequence, image), and libraries (TensorFlow, PyTorch). The benefits of this approach are:\n</p>\n</br>\n<ol>\n\t<li>\n\t\t\u23f1\ufe0f&nbsp;&nbsp;<i>90% reduction in data wrangling</i> via automation of highly conditional and repetitive tasks that vary for each type of dataset and analysis (e.g. model evaluation, metrics, and charts for every split of every model).\n\t</li>\n\t</br>\n\t<li>\n\t\t\ud83d\udcbe&nbsp;&nbsp;<i>Reproducibility</i>, not only because the workflow is persisted (e.g. encoder metadata) but also because it provides standardized classes as opposed to open-ended scripting (e.g. 'X_train, y_test').\n\t</li>\n\t</br>\n\t<li>\n\t\t\ud83c\udf9b\ufe0f&nbsp;&nbsp;<i>No need to install and maintain</i> application and database servers for experiment tracking. SQLite is just a highly-performant and portable file that is automatically configured by `aiqc.setup()`. AIQC is just a pip-installable Python package that works great in Jupyter (or any IDE/shell), and provides a Dash-Plotly user interface (UI) for a <i>real-time experiment tracking</i>.\n\t</li>\n</ol>\n</br>\n\n<h3>What's on the roadmap?</h3>\n<ol>\n\t<li>\n\t\t\ud83d\udda5\ufe0f &nbsp;&nbsp;Expand the UI (e.g. dataset registration and model design) to make it even more approachable for less technical users.\n\t</li>\n\t<li>\n\t \t\u2601\ufe0f&nbsp;&nbsp;Schedule parallel training of models in the cloud.\n\t</li>\n</ol>\n\n</br></br>\n<h1 align='center'>\ud83d\udcda&nbsp;&nbsp;<a href=\"https://docs.aiqc.io\">Documentation</a></h1>\n</br></br>\n\n\n<h2>Experiment Tracker</h2>\n<a href=\"https://docs.aiqc.io\">\n\t<img src=\"https://raw.githubusercontent.com/aiqc/AIQC/main/docs/_static/images/dashboard/experiment_tracker.gif\" width=\"95%\" alt=\"experiment_tracker\"/>\n</a>\n</br></br></br>\n\n<h2>Compare Models</h2>\n<a href=\"https://docs.aiqc.io\">\n\t<img src=\"https://raw.githubusercontent.com/aiqc/AIQC/main/docs/_static/images/dashboard/compare_models.gif\" width=\"95%\" alt=\"compare_models\"/>\n</a>\n</br></br></br>\n\n<h2>What if?</h2>\n<a href=\"https://docs.aiqc.io\">\n\t<img src=\"https://raw.githubusercontent.com/aiqc/AIQC/main/docs/_static/images/dashboard/what_if.gif\" width=\"95%\" alt=\"compare_models\"/>\n</a>\n\n</br></br></br></br>\n\n\n<h2>Usage</h2>\n\n```python\n# Built on Python 3.7.12 to mirror Google Colab\n$ pip install --upgrade pip\n$ pip install --upgrade wheel\n$ pip install --upgrade aiqc\n\n# Monitor and evaluate models (from CLI)\n$ python -m aiqc.ui.app\n```\n\n```python\n# High-level API\nfrom aiqc import mlops\n\n# Declare preprocessing steps\nmlops.Pipeline()\n\n# Define, train, & evaluate models\nmlops.Experiment().run_jobs()\n\n# Infer using original Pipeline\nmlops.Inference()\n```\n\n> Official Installation Documentation:\n>\n> https://aiqc.readthedocs.io/en/latest/notebooks/installation.html\n\n\n</br></br>\n<h1 align='center'>\ud83d\udcda&nbsp;&nbsp;<a href=\"https://docs.aiqc.io\">Documentation</a></h1>\n",
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