Dev tools · alternatives · 2026
24 alternatives to MLEM – ML model deployment tool
Hi, I'm one of the project creators. MLEM is a tool that helps you deploy your ML models. It’s a Python library + Command line tool. 1. MLEM can package an ML model into a Docker image or a Python package, and deploy it to, for example, Heroku. 2. MLEM saves all model metadata to a human-readable text file: Python environment, model methods, model input & output data schema and more. 3. MLEM helps you turn your Git repository into a Model Registry with features like ML model lifecycle management. Our philosophy is that MLOps tools should be built using the Unix approach - each tool solves a…
MLEM is a Python library and command-line tool for deploying machine learning models. It packages models into Docker images or Python packages for deployment to platforms like Heroku, and stores model metadata in… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. MLEM – ML model deployment tool launched in 2022; newer entries below may have overtaken it.
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Deploy a ML inference service on a budget in less than 10 lines of code. - ebhy/budgetml
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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Modelbit▲125Heroku for Data Science, from the founders of Periscope Data
2023 · modelbit.com · its alternatives →
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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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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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ModelHub▲317The missing menu bar app for local LLMs on Mac.
May 2026 · studio.consciousengines.com · its alternatives →
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Machine learning for mobile developers
2018 · developers.google.com · its alternatives →
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I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a compiler that simply converts the model into plain source code with no dependencies, and it actually solved all my deployment problems. Now I'm curious if it can help anyone else too. Through the link you can submit your model file (Keras h5, onnx soon to be supported), and I'll…
2023 · waveworks.dk · its alternatives →
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OrchestraML▲82From English prompt to deployed ML model with human approval
Jun 2026 · orchestra-ml.vercel.app · its alternatives →
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Contribute to xadrianzetx/fullstack.ai development by creating an account on GitHub.
2019 · 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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Hi HN, I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio. The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun. What MLJAR Studio does: - Sets up a local Python environment automatically, runs on Mac, Windows, and Linux - Installs missing packages during the…
May 2026 · mljar.com · its alternatives →
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Comet.ml▲208Automatically track machine learning code and experiments
2018 · comet.com · its alternatives →
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Deploy Django on a cloud server you own. Appliku manages the server, and its CLI and agent skill let your AI coding agent deploy and diagnose your app.
2021 · appliku.com · its alternatives →
- 17MZ
Informasi Akurat, Berita Terupdate – Hanya di Sini
2020 · modelzoo.dev · 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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My Machine Learning Web Service. Contribute to pplonski/my_ml_service development by creating an account on GitHub.
2020 · github.com · its alternatives →
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Open-source, easily create ready-to-use ML models for NLP
2022 · github.com · its alternatives →
Also compare
- Deploy ML Models on a Budget alternatives
- Plexe alternatives
- Igel – A CLI tool to run machine learning without writing code alternatives
- Modelbit alternatives
- Machine learning automation from creating to using models in production alternatives
- ML Blocks – Deploy multimodal AI workflows without code alternatives
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 →