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Based on our record, iko.ai should be more popular than Evidently AI. It has been mentiond 13 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.
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: over 1 year 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
We built a fascinating platform, https://iko.ai, that allows you to train, track, package, deploy, and monitor machine learning models with real-time collaborative notebooks on your own Kubernetes clusters. Source: over 1 year ago
Hi, Edwin. I'm in the process of integrating Stripe to https://iko.ai. I recently discovered Portal (https://stripe.com/docs/billing/subscriptions/integrating-customer-portal) and I thank you for that. Less code for me. I'm a bit ashamed to say, but I'm having trouble with checking if the customer has a valid subscription. I'm currently only storing the customer_id in the database and retrieving the information... - Source: Hacker News / almost 2 years ago
That was one the reasons we do "bring your own compute" with https://iko.ai so people who already have a billing account on AWS, GCP, Azure, DigitalOcean, can just get the config for their Kubernetes clusters and link them to iko.ai and their machine learning workloads will run on whichever cluster they select. If you get a good deal from one cloud provider, you can get started quickly. It's useful even for... - Source: Hacker News / almost 2 years ago
We built an internal platform to streamline this that allows us to train, package, deploy, and monitor models (very shameless plug for our product https://iko.ai that we started because I was tired of watching colleagues look from the window to see if their train was here because they had to come to the office to train their model on the "powerful machine" and they spent 6 hours in commute every day and at some... Source: almost 2 years ago
We built https://iko.ai which offers real-time collaborative notebooks to train, track, package, deploy, and monitor machine learning models. Source: about 2 years ago
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