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The best alternatives to Python Machine Learning are Lobe, Nanonets, and Evidently AI. If these 3 options don't work for you, we've listed over 10 alternatives below.
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PromotedBest alternatives to Python Machine Learning
- Lobe is an easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code.
- Nanonets is an AI-driven solution that automates document processing and data extraction workflows. Leveraging advanced Optical Character Recognition (OCR) and deep learning models, Nanonets helps companies automate document-heavy business processes like accounts payable, order processing and insurance underwriting. Nanonets processes unstructured documents such as invoices, receipts, purchase orders, contracts, claims, and forms and converts them into structured output.
- Evidently helps evaluate, test and monitor your AI-powered products. From ML-based classifiers to LLM chatbots and agents. Built on top of the leading open-source library with over 20 million downloads: https://github.com/evidentlyai/evidently
- Nexosis provides a public API which allows developers to quickly add machine learning to applications.
- The Python Package Index (PyPI) is a repository of software for the Python programming language.
- Anon is an automated machine learning cloud service. It delivers a data scientist as a service, at a fraction of the costs, without needing to have machine learning knowledge.
- Datapane is a python framework that converts your Jupyter Notebook into a beautiful, shareable, and interactive data app in seconds.
- Continual is the easiest way to build continually improving predictive models - from customer churn to inventory forecasts - directly on your cloud data warehouse.
- We publish one machine learning question every day. That's it! You load the page, answer the question, and return the next day. Rinse and repeat.
- Web-base annotation platform for sharing and creating datasets at any scale.
- Efemarai is a visual, 3D machine learning debugger that allows you to inspect your models as they train or make predictions. You can visualize models and data in 3D, step through your code to find issues and understand why your model is behaving the way it is.
