Dev tools · alternatives · 2026

24 alternatives to automl-docker

Open-source, easily create ready-to-use ML models for NLP

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. automl-docker launched in 2022; newer entries below may have overtaken it.

1
Cargoship▲117
Curated open-source AI models for web developers
2023 · its alternatives →
2
Igel – A CLI tool to run machine learning without writing code▲405
a delightful machine learning tool that allows you to train, test, and use models without writing code - nidhaloff/igel
2020 · github.com · its alternatives →
3
Ollama – Run LLMs on your Mac▲284
Hi HN A few folks and I have been working on this project for a couple weeks now. After previously working on the Docker project for a number of years (both on the container runtime and image registry side), the recent rise in open source language models made us think something similar needed to exist for large language models too. While not exactly the same as running linux containers, running LLMs shares quite a few of the same challenges. There are "base layers" (e.g. models like Llama 2), specific configuration to run correctly (parameters, temperature, context window sizes etc). There's…
2023 · github.com · its alternatives →
4
Plexe▲273
Build and deploy ML models in English
Oct 2025 · plexe.ai · its alternatives →
5
ZenML▲84
Create reproducible machine learning pipelines
2020 · github.com · its alternatives →
6
Web App with GUI for AutoML on Tabular Data▲41
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 →
7
OrchestraML▲82
From English prompt to deployed ML model with human approval
Jun 2026 · orchestra-ml.vercel.app · its alternatives →
8
Autolabel, a Python library to label and enrich text data with LLMs▲153
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 →
9
An annotation tool for ML and NLP▲76
The easy way to turn text into structure data
2021 · getmarkup.com · its alternatives →
10
Plexe – ML Models from a Prompt▲130
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 →
11
MakeML▲274
Train Neural Networks without a line of code
2019 · makeml.app · its alternatives →
12
AutoML Python Package for Tabular Data with Automatic Documentation▲67
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation - mljar/mljar-supervised
2022 · github.com · its alternatives →
13
Machine learning automation from creating to using models in production▲72
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 →
14
Google CLOUD AUTOML▲93
Train custom ML models with minimum effort and expertise
2018 · cloud.google.com · its alternatives →
15
UnionML▲77
The easiest way to build and deploy ML microservices
2022 · union.ai · its alternatives →
16
SapientML – Generative AutoML for Tabular Data▲82
Generative AutoML for Tabular Data. Contribute to sapientml/sapientml development by creating an account on GitHub.
2023 · github.com · its alternatives →
17
CLI for working with Apple Core ML models▲48
A native command-line interface for working with Apple Core ML models on macOS - schappim/coreml-cli
Jan 2026 · github.com · its alternatives →
18
Cluster▲72
Automagically create docs for your open source project
2023 · getcluster.ai · its alternatives →
19
Fritz▲161
Mobile machine learning made easy
2018 · fritz.ai · its alternatives →
20
ScoopML▲81
An AI assistant for machine learning engineers
2020 · its alternatives →
21
Pretrained AI▲144
Integrate pretrained machine learning models in minutes.
2021 · pretrained.ai · its alternatives →
22
AIQ – A no-frills CLI for embeddings and text classification▲91
aiq lets you use a single CLI command to auto-label text data with a language model API & train an efficient classifier on the labels. Training and inference are super fast (both run on CPU).
2024 · github.com · its alternatives →
23
MLConsole – web app to train ML models, for free and client-side▲86
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 →
24
Doccupine▲98
Open source AI-ready documentation platform.
Mar 2026 · doccupine.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 →