Based on our record, Datature should be more popular than Evidently AI. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Of course, you can write your own code, in that case, think of it as an interactive matplotlib then! Also, it helps to mention we run a startup Datature, that is a no-code MLOps platform, hence explaining why we are focusing on removing the coding portion of this process :P. Source: almost 3 years ago
A while ago, we announced here that we built Datature and a bunch of users gave feedback and even built MaskRCNN models on our platform! However, we were sending collab updates back and forth - it was a mess. Hence we made Portal for any TensorFlow users to load TF2.0 models (any models off TF2 Model Hub works) and inspect your model visually on your dataset. Source: almost 3 years ago
If you'd like to train a tensorflow object detection model, you can check out https://datature.io - theres about 30 different models you can select from and you can add augmentation to your pipeline. Source: about 3 years ago
If you will be training an object detection model at the end, you can check out https://datature.io - you can annotate your data in browser (no installation) and train an object detection model + deploy when you are done for free! Source: about 3 years ago
Feel free to try it out at https://datature.io - additionally, we are always looking out for feedback and feature requests. We are working more MLOps feature to support teams, so let us know of your thoughts :). Source: about 3 years ago
It is doable. However the main focus of MLFlow is in experiment tracking. I would suggest for you to look into another monitoring tools such evidentlyai . You can track more things than performance (e.g.data drift). Which may be helpful in a production setting. Source: almost 2 years ago
Evidently is an open-source Python library that analyzes and monitors machine learning models. It generates interactive reports based on Panda DataFrames and CSV files for troubleshooting models and checking data integrity. These reports show model health, data drift, target drift, data integrity, feature analysis, and performance by segment. - Source: dev.to / over 2 years ago
Colornet - Neural Network to colorize grayscale images
ML Showcase - A curated collection of machine learning projects
DALL-E - Creating images from text, from Open AI
ML5.js - Friendly machine learning for the web
Quick Draw Game - Can a neural network learn to recognize doodles?
Censius.ai - Building the future of MLOps