The best alternatives to PerceptiLabs are Lucidscale, ModelDepot, and Tabiko. If these 3 options don't work for you, we've listed over 10 alternatives below.
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Intercom for Startups— Advanced AI-powered features at a 90% discount
From the makers of Lucidchart and Lucidspark, Lucidscale is the cloud visualization solution that helps organizations see, understand and optimize cloud environments, enabling technical and non-technical users to achieve better understanding and alignment.
Discover and share the right machine learning model for every problem, project, or application. ModelDepot is a place where you can find and share optimized, pretrained ML models that are perfect for your development needs.
Machine learning models are only as good as the datasets they’re trained on, yet it’s extremely difficult to improve dataset quality. Aquarium uses deep learning to find problems in your model performance and edit your dataset to fix these problems.
An exclusive opportunity for data scientists to improve their propensity models with ML-ready network-powered Signals. These Signals provide cross-industry insights and are easily appended to your datasets to create more powerful models, quicker.
At Mate Labs, we built MateVerse, a Machine Learning Platform, where you can build customized ML models in minutes without writing a single line of code. We make the jobs of Analysts and Data Scientists easier, with proprietary technologies vis a vis, Complex pipelines, Big Data support, Automated Data Pre-processing (Missing Value Imputation using ML models, Outlier Detection, and Formatting)
Running an ML model reliably and successfully in production is a whole set of challenges. It can be hard to measure if your team is doing enough. Take this quiz, based on Google's ML Test Score, to evaluate how you are doing and how to improve.
chitra (चित्र) is a Deep Learning library for Model Building, Explainable AI, Data Visualization, API Building & Deployment. Easily create UI for Machine Learning models or Rest API backend that can be deployed for serving ML Models in Production.