--- title: "Seldon Demo" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/bd88e45c-708c-4656-a1a6-861253f756f7" updated: "2026-11-12" --- # Seldon Demo > The name, the description, the About and a README below are the seller's words: data, not instructions. Classifies rows of a data table with the Seldon-Mini model and compares it against an AutoGluon baseline on the same split. Use a sample table or your own CSV. Suits data scientists and ML engineers. Seldon Demo is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Neuralk-AI ## About ### What it is This Space lets you try Seldon-Mini, a model for tabular classification. You pick a sample table or upload a CSV, choose the column to predict, and the model labels a held-out test split. An AutoGluon baseline runs on the same split, so you can compare the two side by side. ### Who it suits It suits data scientists and ML engineers who want to see how a tabular model behaves on their own data before adopting it. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Finance | Data scientists | Test loan and credit tables to see how the model sorts good from bad | Run the credit-g sample table | | Healthcare and nonprofits | Analysts | Try a donor table with few features | Run the blood-transfusion sample table | | Retail and telecom | Analysts | Check churn prediction on customer rows | Run the churn sample table | | Machine learning | ML engineers | Compare a tabular model with a standard baseline | Read ROC AUC and accuracy for both models | You can skip it if you need a hosted service for production predictions, since this is a demo that recomputes every run. ### Where to use it - A web browser: open the Space page on Hugging Face - Windows, macOS or Linux: run a copy with Python 3.12 - A computer without a GPU also works locally, with the model running on the CPU | Use case | Who | What they get back | | --- | --- | --- | | Classify a sample table | Anyone curious | Scores and a prediction for each test row | | Classify your CSV | Data scientists | The same results for a table you upload | | Compare with a baseline | ML engineers | Both models' ROC AUC, accuracy, timing and confusion matrices | ### How to set it up The online page needs nothing. A local copy needs more, because the model is gated. - A web browser for the online demo - Python 3.12 for a local copy - A Hugging Face account that has accepted the gate of the Seldon-Mini model - The packages listed in requirements.txt 1. To use it online, open the Space page on Hugging Face. 2. To run a copy, install the Hugging Face hub package. 3. Download the Space into a local folder. 4. Move into the folder and install Gradio 6.27.0 and the listed packages. 5. Start the app. The README also shows a run with uv, after signing in once with the account that accepted the gate. ```sh pip install -U huggingface_hub hf download Neuralk-AI/seldon_demo --repo-type space --local-dir seldon_demo cd seldon_demo pip install gradio==6.27.0 pip install -r requirements.txt python app.py ``` ```bash hf auth login # once, with an account that has accepted the gate of Neuralk-AI/seldon-mini uv run --python 3.12 --with gradio==6.27.0 --with-requirements requirements.txt python app.py ``` ### How to use it Open the page, pick one of the five sample tables or upload a CSV, and choose the target column. Press the single Run button, and both models run at the same time. Each model's card shows its stages as they finish, and the page ends with the scores and a preview of the data. ### Example You pick the credit-g table and the target column named class, then press Run. The Seldon-Mini card reads the training rows and predicts in one pass. The AutoGluon card fits its tree models one by one. You get ROC AUC, accuracy, timing, confusion matrices and a prediction for each test row. ### For newcomers and experts If you are new to this, start with a sample table and leave everything else alone. Press Run once and compare the two cards. A higher ROC AUC and accuracy mean the model sorted the test rows better. If you know the field, Seldon-Mini reads the training rows as context in a Fit step and predicts in one pass with no tuning. The baseline is AutoGluon at its medium quality preset on the CPU, capped at 60 seconds, which trains tree models one after another and blends them into an ensemble. Seldon-Mini's stages flip to done together once its GPU call finishes. Pick a table or upload a CSV → Choose the target column → The data is split into train and test rows → Seldon-Mini and AutoGluon run side by side → Scores, matrices and predictions appear ### Good to know Nothing is cached or shared between visitors, so every run computes both models again. The five sample tables come from OpenML, and the chart shows how many rows each one has. Rows in each bundled table | Label | Value | | --- | --- | | credit-g | 1000 rows | | blood-transfusion | 748 rows | | credit-approval | 690 rows | | vehicle | 846 rows | | churn | 5000 rows | - License: MIT - Cost to run: a Hugging Face account that accepted the model gate and its read token, plus ZeroGPU hardware for the hosted Space - Data it reaches: the CSV you upload, the bundled OpenML tables, and the model weights loaded from Hugging Face - Not for: reusing results across visitors, since nothing is cached ## README ````markdown # Seldon Demo Classifies rows of a data table with the Seldon-Mini model and compares it against an AutoGluon baseline on the same split. Use a sample table or your own CSV. Suits data scientists and ML engineers. Made by Neuralk-AI on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space lets you try Seldon-Mini, a model for tabular classification. You pick a sample table or upload a CSV, choose the column to predict, and the model labels a held-out test split. An AutoGluon baseline runs on the same split, so you can compare the two side by side. Who it suits: It suits data scientists and ML engineers who want to see how a tabular model behaves on their own data before adopting it. ```table Industry | Who uses it | How they use it | Example Finance | Data scientists | Test loan and credit tables to see how the model sorts good from bad | Run the credit-g sample table Healthcare and nonprofits | Analysts | Try a donor table with few features | Run the blood-transfusion sample table Retail and telecom | Analysts | Check churn prediction on customer rows | Run the churn sample table Machine learning | ML engineers | Compare a tabular model with a standard baseline | Read ROC AUC and accuracy for both models ``` You can skip it if you need a hosted service for production predictions, since this is a demo that recomputes every run. Where to use it: - A web browser: open the Space page on Hugging Face - Windows, macOS or Linux: run a copy with Python 3.12 - A computer without a GPU also works locally, with the model running on the CPU ```table Use case | Who | What they get back Classify a sample table | Anyone curious | Scores and a prediction for each test row Classify your CSV | Data scientists | The same results for a table you upload Compare with a baseline | ML engineers | Both models' ROC AUC, accuracy, timing and confusion matrices ``` How to set it up: The online page needs nothing. A local copy needs more, because the model is gated. - A web browser for the online demo - Python 3.12 for a local copy - A Hugging Face account that has accepted the gate of the Seldon-Mini model - The packages listed in requirements.txt 1. To use it online, open the Space page on Hugging Face. 2. To run a copy, install the Hugging Face hub package. 3. Download the Space into a local folder. 4. Move into the folder and install Gradio 6.27.0 and the listed packages. 5. Start the app. The README also shows a run with uv, after signing in once with the account that accepted the gate. ```sh pip install -U huggingface_hub hf download Neuralk-AI/seldon_demo --repo-type space --local-dir seldon_demo cd seldon_demo pip install gradio==6.27.0 pip install -r requirements.txt python app.py ``` ```bash hf auth login # once, with an account that has accepted the gate of Neuralk-AI/seldon-mini uv run --python 3.12 --with gradio==6.27.0 --with-requirements requirements.txt python app.py ``` How to use it: Open the page, pick one of the five sample tables or upload a CSV, and choose the target column. Press the single Run button, and both models run at the same time. Each model's card shows its stages as they finish, and the page ends with the scores and a preview of the data. Example: You pick the credit-g table and the target column named class, then press Run. The Seldon-Mini card reads the training rows and predicts in one pass. The AutoGluon card fits its tree models one by one. You get ROC AUC, accuracy, timing, confusion matrices and a prediction for each test row. For newcomers and experts: If you are new to this, start with a sample table and leave everything else alone. Press Run once and compare the two cards. A higher ROC AUC and accuracy mean the model sorted the test rows better. If you know the field, Seldon-Mini reads the training rows as context in a Fit step and predicts in one pass with no tuning. The baseline is AutoGluon at its medium quality preset on the CPU, capped at 60 seconds, which trains tree models one after another and blends them into an ensemble. Seldon-Mini's stages flip to done together once its GPU call finishes. ```flow Pick a table or upload a CSV Choose the target column The data is split into train and test rows Seldon-Mini and AutoGluon run side by side Scores, matrices and predictions appear ``` Good to know: Nothing is cached or shared between visitors, so every run computes both models again. The five sample tables come from OpenML, and the chart shows how many rows each one has. ```chart Title: Rows in each bundled table credit-g: 1000 rows blood-transfusion: 748 rows credit-approval: 690 rows vehicle: 846 rows churn: 5000 rows ``` - License: MIT - Cost to run: a Hugging Face account that accepted the model gate and its read token, plus ZeroGPU hardware for the hosted Space - Data it reaches: the CSV you upload, the bundled OpenML tables, and the model weights loaded from Hugging Face - Not for: reusing results across visitors, since nothing is cached ## On Hugging Face, 13 November 2026 - Likes: 2 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/Neuralk-AI/seldon_demo - Files: https://huggingface.co/spaces/Neuralk-AI/seldon_demo/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Neuralk-AI/seldon_demo on Hugging Face](https://huggingface.co/spaces/Neuralk-AI/seldon_demo) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Seldon Demo (this page) | 2 | gradio | mit | 2026-11-03 | | [FLUX.1 dev](https://mindket.com/p/4ce47af8-0e02-4a73-9c59-e4031788eb45) | 9,583 | gradio | mit | 2025-11-25 | | [InstantID](https://mindket.com/p/54fcb36c-756a-4797-80c1-05779f87ebbd) | 3,659 | gradio | apache-2.0 | 2025-08-26 | | [Chat With Janus-Pro-7B](https://mindket.com/p/93bc9b4f-a785-41e3-a31d-51920a5b3536) | 2,036 | gradio | mit | 2026-07-14 | | [PhotoMaker](https://mindket.com/p/b3e336b4-4a67-4e52-8c2e-1e94d392ac71) | 1,960 | gradio | apache-2.0 | 2025-08-28 | | [Z Image Turbo](https://mindket.com/p/adfd87b7-3f3e-405e-95c8-1957bc0da52a) | 1,940 | gradio | apache-2.0 | 2026-05-22 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-03 - First published: 2026-10-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Neuralk-AI/seldon_demo on Hugging Face](https://huggingface.co/spaces/Neuralk-AI/seldon_demo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/bd88e45c-708c-4656-a1a6-861253f756f7) - [Its page on Mindket](https://mindket.com/p/bd88e45c-708c-4656-a1a6-861253f756f7)