No Deploifai videos yet. You could help us improve this page by suggesting one.
Based on our record, Evidently AI should be more popular than Deploifai. It has been mentiond 2 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.
I have been building Deploifai for a year. I built it for myself early on because I wanted to train machine learning models on the cloud since we don't have the resources for a physical machine. I basically wanted to use my AWS account to create VMs with environments pre-configured, and just simply start building my ML models. Deploifai sets up the VM with pre-selected ML framework, NVIDIA drivers and Jupyterlab.... - Source: Hacker News / over 2 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
Labelf AI - The Labelf AI Platform aims to let anyone, no matter previous knowledge, create and use AI text classification models.
ML Showcase - A curated collection of machine learning projects
Generated.photos - Explore our free resource of 100k high-quality faces, each entirely generated by AI. Use them in your projects, mockups, or wherever — all for just a link back to us!
Censius.ai - Building the future of MLOps
Mona - Personalized shopping app that goes on shopping missions
iko.ai - Real-time collaborative notebooks on your own Kubernetes clusters to train, track, package, deploy, and monitor your machine learning models.