CloudStack
OpenStack
Amazon EC2
OpenShift
Google Compute Engine
Hostwinds
Docker Compose
AlwaysData
neptune.ai
Algorithmia
Comet.ml
Spell
MCenter
5Analytics
Managed MLflow
Numericcal
Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.
Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code
Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.
CloudStack neptune.aiCloudStack is recommended for enterprises and service providers that need a customizable and scalable cloud solution. It is particularly suitable for those who require support for multiple hypervisors and need to integrate with existing infrastructure components. It is also ideal for organizations preferring open-source solutions with active community support.
Only negative is I didn't see it integrated with Azure, does with Google, AWS and one more. Looks real nice, and pretty powerful and plenty useful features for a data science group
Based on our record, neptune.ai seems to be a lot more popular than CloudStack. While we know about 24 links to neptune.ai, we've tracked only 1 mention of CloudStack. 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.
You could look at the Apache Cloudstack project Https://cloudstack.apache.org/index.html. Source: over 4 years ago
Some tools for model validation include Neptune AI, Kolena, and Censius. - Source: dev.to / over 1 year ago
Experiment tracking tools like MLflow, Weights and Biases, and Neptune.ai provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach. - Source: dev.to / over 2 years ago
Neptune.ai - Log, store, display, organize, compare, and query all your MLOps metadata. Free for individuals: 1 member, 100 GB of metadata storage, 200h of monitoring/month. - Source: dev.to / over 2 years ago
Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/. Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase,... - Source: Hacker News / almost 3 years ago
There are a lot of tools out there for experiment tracking (eg neptune.ai), but I'm really not sure whether that sort of thing is over the top for what I need to do. Source: about 3 years ago
OpenStack - OpenStack software controls large pools of compute, storage, and networking resources throughout a datacenter, managed through a dashboard or via the OpenStack API.
Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.
Amazon EC2 - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.
Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.
OpenShift - OpenShift gives you all the tools you need to develop, host and scale your apps in the public or private cloud. Get started today.
Spell - Deep Learning and AI accessible to everyone