Dokku VS Comet.ml
Compare Dokku VS Comet.ml and see what are their differences
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Dokku
Google App Engine
Salesforce Platform
Google Cloud Functions
AWS Lambda
Azure Web Apps
Heroku
CapRover
Comet.ml
neptune.ai
Weights & Biases
Managed MLflow
Spell
Algorithmia
Iterative.ai
MCenter
DokkuBased on our record, Dokku seems to be more popular. It has been mentiond 12 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.
Yeah there are a bunch of selfhostable things: Caprover (https://caprover.com/) Dokku (https://github.com/dokku/dokku. - Source: Hacker News / 2 months ago
Considering other orchestration tools like dokku, dcos, deis, flynn, docker swarm, etc.. Kubernetes is no where near to them in terms of lines of code, on an average those tools are around 100k-200k lines of code. Source: over 1 year ago
Other interesting projects to also follow: * Caprover * Dokku. Source: over 1 year ago
If I could make a recommendation, it would be to give Dokku a try. (Disclaimer: not affiliated, but like the project so much I sponsor it. My opinions are biased towards it.). Source: almost 2 years ago
My next favorite option is to host on a DigitalOcean VM. You can use Dokku to get your own mini-Heroku PaaS, or manage the VM yourself (following Microsoft's documentation). You can get a $100 60-day credit from a referral link - A good way to get started. Source: almost 2 years ago
Google App Engine - A powerful platform to build web and mobile apps that scale automatically.
neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.
Salesforce Platform - Salesforce Platform is a comprehensive PaaS solution that paves the way for the developers to test, build, and mitigate the issues in the cloud application before the final deployment.
Weights & Biases - Developer tools for deep learning research
Google Cloud Functions - A serverless platform for building event-based microservices.
Managed MLflow - Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.