AI · alternatives · 2026
24 alternatives to Unweave
ML, without the FML
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Unweave launched in 2022; 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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Machine learning for mobile developers
2018 · developers.google.com · 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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We get asked whether we support Notebooks on Unweave all the time. So, we put together a serverless Jupyter Lab Portal built on top of Unweave. You can choose from 5 different GPUs or use CPU only. The lab comes with an `uwstore` folder at the root of the repository. Any files you add there will be persisted across multiple sessions. We've pre-loaded all labs with a set of commonly used ML dependencies. If you'd like to add additional dependencies, you can install them directly in the Jupyter Lab and save them to a `requirements.txt` or `environment.yml` file. Unweave will automatically…
2022 · playground.unweave.io · its alternatives →
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Comet.ml▲208Automatically track machine learning code and experiments
2018 · comet.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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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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2021 · convect.ml · its alternatives →
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Guide to build a machine learning app in 30 min without code
2018 · airdev.co · 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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- 20MM
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…
2022 · its alternatives →
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The simplest way to serve AI/ML models in production - basetenlabs/truss
2022 · 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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latch.ai▲84Train, track and serve machine learning on the cloud.
2021 · latch.ai · 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 →