Life & fun · alternatives · 2026
24 alternatives to Tangram – Train a model from a CSV file on the command line
Tangram is a command-line tool that lets users train machine learning models directly from CSV files without extensive coding. Designed for developers and data analysts who want to build predictive models quickly, it… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Tangram – Train a model from a CSV file on the command line launched in 2021; newer entries below may have overtaken it.
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a delightful machine learning tool that allows you to train, test, and use models without writing code - nidhaloff/igel
2020 · github.com · its alternatives →
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2020 · colab.research.google.com · its alternatives →
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Mengram▲108AI memory that learns how you work — facts, events, and workflows that improve from failures. One memory across Claude Code, Cursor, Codex, and ChatGPT. Free tier, $5/mo.
Feb 2026 · mengram.io · its alternatives →
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Bring DevOps practices to your projects for automatic, reproducible, and fast machine learning.
2020 · cml.dev · its alternatives →
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2019 · beta.aifiddle.io · its alternatives →
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Provide an input CSV and a target field to predict, generate a model + code to run it. - minimaxir/automl-gs
2019 · github.com · its alternatives →
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The goal of Carton is to let you use a single interface to run any machine learning model from any programming language. It’s currently difficult to integrate models that use different technologies (e.g. TensorRT, Ludwig, TorchScript, JAX, GGML, etc) into your application, especially if you’re not using Python. Even if you learn the details of integrating each of these frameworks, running multiple frameworks in one process can cause hard-to-debug crashes. Ideally, the ML framework a model was developed in should just be an implementation detail. Carton lets you decouple your application from…
2023 · carton.run · its alternatives →
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Keras code and weights files for popular deep learning models. - fchollet/deep-learning-models
2016 · github.com · its alternatives →
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A native command-line interface for working with Apple Core ML models on macOS - schappim/coreml-cli
Jan 2026 · github.com · its alternatives →
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Large Language Models (LLMs) are powerful, but they’re limited by fixed context windows and outdated knowledge. What if your AI could access live search, structured data extraction, OCR, and more—all through a standardized interface? We built the JigsawStack MCP Server, an open-source implementation of the Model Context Protocol (MCP) that lets any AI model call external tools effortlessly. Here’s what it unlocks: - Web Search & Scraping: Fetch live information and extract structured data from web pages. - OCR & Structured Data Extraction: Process images, receipts, invoices, and handwritten…
2025 · its alternatives →
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Improved file parsing for LLM’s. Contribute to Filimoa/open-parse development by creating an account on GitHub.
2024 · github.com · its alternatives →
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I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license:…
2025 · nature.com · its alternatives →
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Hey everyone, ML Blocks is a node-based workflow builder to create multi-modal AI workflows without writing any code. You connect blocks that call various visual models like GPT4v, Segment Anything, Dino etc. along with basic image processing blocks like resize, invert color, blur, crop, and several others. The idea is to make it easier to deploy multi-step image processing workflows, without needing to spin up endless custom OpenCV cloud functions to glue together AI models. Usually, even if you're using cloud inference servers like Replicate, you still need to write your own image…
2024 · mlblocks.com · its alternatives →
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Hey HN! We’re Vaibhav and Marcello. We’re building Plexe (https://github.com/plexe-ai/plexe), an open-source agent that turns natural language task descriptions into trained ML models. Here’s a video walkthrough: https://www.youtube.com/watch?v=bUwCSglhcXY. There are all kinds of uses for ML models that never get realized because the process of making them is messy and convoluted. You can spend months trying to find the data, clean it, experiment with models and deploy to production, only to find out that your project has been binned for taking so long.…
2025 · github.com · its alternatives →
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ML Console is a free tool to build an AI model without writing a single line of code. Simply pick a column to predict, and let us handle the rest.
2021 · mlconsole.com · its alternatives →
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NeuroBlock▲120No-code AI Lab: Train models, access datasets, run inference
Feb 2026 · neuro-block.com · its alternatives →
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A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python. - reiinakano/xcessiv
2017 · github.com · its alternatives →
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built a tiny pytorch clone in c after going through prof. vijay janapa reddi's mlsys book: mlsysbook.ai/tinytorch/ perfect for learning how ml frameworks work under the hood :)
Dec 2025 · github.com · its alternatives →
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We’ve built a Python SDK for running inference on foundation models designed for time-series and tabular data. They are new SOTA models for time-series and tabular tasks and work out of the box. They do not require model training or feature engineering. The link to the GitHub repository is: https://github.com/S-FM/faim-python-client
Dec 2025 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →