AI · alternatives · 2026
24 alternatives to Python library to scan ML models for vulnerabilities
Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to…
This Python library automatically scans machine learning models to identify vulnerabilities including underperforming data subsets, prediction overconfidence, and robustness issues. It works with major ML frameworks… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Python library to scan ML models for vulnerabilities launched in 2023; newer entries below may have overtaken it.
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🐢 Open-Source Evaluation & Testing library for LLM Agents - Giskard-AI/giskard-oss
2023 · github.com · its alternatives →
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Open source tool for testing & validating your model & data
2022 · github.com · its alternatives →
- 4CR
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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Machine Learning library for Rust. Contribute to AtheMathmo/rusty-machine development by creating an account on GitHub.
2016 · github.com · its alternatives →
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Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation. - blobcity/autoai
2021 · github.com · its alternatives →
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Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance. Some of the key features of PyBroker include: - A super-fast backtesting engine built using NumPy and accelerated with Numba. - The ability to create and execute trading rules and models across multiple instruments with ease. - Access to…
2023 · github.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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OSS Python library for detecting silent ML model failure
2022 · nannyml.com · its alternatives →
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All-in-one data annotation platform for machine learning
2019 · its alternatives →
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A Python toolkit for rule-based/unsupervised anomaly detection in time series - arundo/adtk
2019 · github.com · its alternatives →
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I've made a small Python library, designed for quick-and-easy prototyping of machine learning models. It's built on top of scikit-learn, to serialize and deserialize data from the forms you're likely to have, to the format used in scikit-learn. https://github.com/madman-bob/Smart-Fruit It's pretty bare-bones at the moment, but I thought I'd see if there was any interest before spending too much time on it. Let me know what you think.
2018 · its alternatives →
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I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special…
2023 · github.com · its alternatives →
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Learning machine learning has never been easier
2017 · amazon.com · its alternatives →
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GoLearn▲81Machine Learning for Go. Contribute to sjwhitworth/golearn development by creating an account on GitHub.
2014 · github.com · its alternatives →
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I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.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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Vibe profile your ML models to get max performance.
2025 · github.com · its alternatives →
- 19UA
Hello! So, I am experimenting with new approaches to automated machine learning, where you don't need anything besides either data or prompt This is a first platform from my auto ml serie called Łukasiewicz (Jan Łukasiewicz was a logician who created Polish notation) I much appreciate your feedback and if you want to try it out, I created a -75% off promo code "HACKERNEWS" on credits, so it hopefully will cover at least some of computing expenses, but more importantly will let you play with the platform at almost no cost Thank you!
2024 · lukasiewicz.tmlc.pl · its alternatives →
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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 →
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Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation - mljar/mljar-supervised
2021 · github.com · its alternatives →
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Deep-ML▲13We just launched Deep-ML Labs, a new section on Deep-ML Each Lab is a hands-on challenge where you implement parts of real models, activations, layers, optimizers, normalization, even small transformers — directly in PyTorch (and sometimes NumPy). You can test your implementations on actual datasets, see how your results compare to others, and understand what’s really happening under the hood of deep learning frameworks. let me know what you guys think and if we should work on making more of these questions
2025 · deep-ml.com · its alternatives →
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A minimalist deep learning library written from scratch in Python - sradc/SmallPebble
2021 · github.com · its alternatives →
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PwnScan▲8For at least a few years (since before COVID), in my circle everbody wants an AI tool that finds vulnerabilities in binaries. I have my reservations about such a tool because of my perception that AI is overhyped, so I decided to build one myself to see how useful it could be (or not). I was actually kinda surprised how well it worked for my collection of binaries with known CVEs. It definitely has false positives and negatives, as any static analysis tool would, but I think its actually somewhat usable. I dont think the results are super actionable, but interesting nonetheless. I still…
2025 · pwnscan.com · its alternatives →
Also compare
- Giskard alternatives
- Giskard – Testing framework dedicated to LLMs and Tabular ML models alternatives
- Deepchecks Testing Package alternatives
- Carton – Run any ML model from any programming language alternatives
- Machine learning (entirely) in Rust alternatives
- AutoAI – A framework to find the best performing AI/ML model 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 →