AI · alternatives · 2026
24 alternatives to Igel – A CLI tool to run machine learning without writing code
a delightful machine learning tool that allows you to train, test, and use models without writing code - nidhaloff/igel
Igel is a command-line tool that enables users to build and run machine learning models without writing code. Launched in 2020, it simplifies the ML workflow by allowing developers and data scientists to configure… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Igel – A CLI tool to run machine learning without writing code launched in 2020; 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 - GitHub - nidhaloff/igel at v0.4.0
2021 · github.com · its alternatives →
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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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The fastest, most precise no-code AI in the world.
2020 · obviously.ai · its alternatives →
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Codex by OpenAI▲954OpenAI's smartest and most intuitive to use model yet
2021 · openai.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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LLM 0.26 is out with the biggest new feature since I started the project: support for tools. You can now use the LLM CLI tool—and Python library—to grant LLMs from …
2025 · simonwillison.net · its alternatives →
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Build AI models and get predictions, no code required
2023 · mentana.ai · its alternatives →
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AutoML Web App - build Machine Learning pipeline in automatic way with Graphical User Interface (GUI). You can run app locally! - mljar/automl-app
2023 · github.com · its alternatives →
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ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io. - zenml-io/zenml
2020 · github.com · its alternatives →
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Unlock the power of AI with no-code machine learning
2022 · graphite-note.com · its alternatives →
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Weave▲237Hey HN, We’re building Weave: an ML-powered tool to measure engineering output, that actually understands engineering output! Why? Here’s the thing: almost every eng leader already measures output - either openly or behind closed doors. But they rely on metrics like lines of code (correlation with effort: ~0.3), number of PRs, or story points (slightly better at ~0.35). These metrics are, frankly, terrible proxies for productivity. We’ve developed a custom model that analyzes code and its impact directly, with a far better 0.94 correlation. The result? A standardized engineering output…
2025 · app.workweave.ai · its alternatives →
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I wrote this tool to get familiar with CLIP model, I know many people have written similar tools with CLIP before, but I'm new to machine learning and writing a classic tool helps my study. The unusual thing with my version is, it is in pure Node.js, with the power of node-mlx, a Node.js machine learning framework. The repo in the link is mostly about implementing indexing and CLI, the code of the model implementation lives as a Node.js module: https://github.com/frost-beta/clip . Hope this helps other learners!
2024 · github.com · 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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Comet.ml▲208Automatically track machine learning code and experiments
2018 · comet.com · its alternatives →
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Hi HN! I'm excited to share Autolabel, an open-source Python library to label and enrich text datasets with any Large Language Model (LLM) of your choice. We built Autolabel because access to clean, labeled data is a huge bottleneck for most ML/data science teams. The most capable LLMs are able to label data with high accuracy, and at a fraction of the cost and time compared to manual labeling. With Autolabel, you can leverage LLMs to label any text dataset with <5 lines of code. We’re eager for your feedback!
2023 · github.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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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 →