neptune.ai
Comet.ml
Algorithmia
Weights & Biases
Managed MLflow
Spell
MCenter
Iterative.ai
Google App Engine
Salesforce Platform
Heroku
Dokku
AWS Lambda
AWS Elastic Beanstalk
Google Cloud Functions
Azure Web Apps
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.
neptune.ai Google App EngineOnly 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
Google App Engine might be a bit more popular than neptune.ai. We know about 25 links to it since March 2021 and only 22 links to neptune.ai. 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.
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 / 3 months 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 / 7 months 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: 8 months ago
Welcome to another episode of The Developer-led Podcast, where we dive into the strategies modern companies use to build and grow their developer tools. In this exciting episode, we're joined by Jakub Czakon, the CMO at Neptune.ai, a startup that assists developers in efficiently managing their machine-learning model data. Jakub is renowned not only for his role at Neptune.ai but also for his developer marketing... - Source: dev.to / 9 months ago
Tbh I have done a pretty good search on this topic, I couldn't find any. I thought maybe community could help me find one, if people like you (who works at neptune.ai) have the same opinion then it is what it is :). Anyway thank you for the suggestions that you gave, probably gonna use that. Source: 10 months ago
To deploy the app, we can use Google Cloud App Engine, which is specifically built for server-side rendered websites. After we create a new project in the Google Cloud Console, we have to configure the cql-trace-viewer application. - Source: dev.to / 11 months ago
I've read that article, but I'm thinking there are other better (and most importantly cheaper) ways of doing that, such as using App Engine (given that you have to mitigate the maximum request timeout and to make sure there are constantly exactly 1 instance running). Source: 12 months ago
Shout out to GCP App Engine for deploying anode/Express severe. Source: 12 months ago
If your project is a bit more complicated using next.js or react.js or angular.js, you may find some free Platfrom-as-a-Service%20is%20a%20complete%20cloud%20environment,middleware%2C%20tools%2C%20and%20more.). I have seen some of my peers using free PaaS like Heroku, Vercel and I have no experience in using PaaS but I will recommend you to use PaaS from either of the three 1. Google Cloud's Google App Engine 2.... Source: about 1 year ago
UNIX is irrelevant on the cloud, unless one is stuck deploying legacy workloads on VMs, this is what we use in modern applications not stuck in the past. https://aws.amazon.com/eks/ https://azure.microsoft.com/en-us/products/kubernetes-service https://cloud.google.com/kubernetes-engine/ https://cloud.google.com/appengine https://azure.microsoft.com/en-us/products/app-service https://aws.amazon.com/lambda/... - Source: Hacker News / about 1 year ago
Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.
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.
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.
Heroku - Agile deployment platform for Ruby, Node.js, Clojure, Java, Python, and Scala. Setup takes only minutes and deploys are instant through git. Leave tedious server maintenance to Heroku and focus on your code.
Weights & Biases - Developer tools for deep learning research
Dokku - Docker powered mini-Heroku in around 100 lines of Bash