We are the company behind very popular open-source tools for ML workflow- DVC.org and CML.dev. We are solving the complexity of managing datasets, ML infrastructure, ML models lifecycle management. We are Hashicorp for ML.
Join our well-funded remote-first DVC team if you love building open source, developer tools, and CLI. If you want to see how your code is used by thousands of developers every day! If you don't mind Python.
This is excellent! You put into words all those benefits I was trying to communicate a while ago (https://twitter.com/shcheklein/status/1325978612378423297) but didn't find the right term . Documentation is an overloaded term (means everything from readme to man pages) and I guess, that's why it makes it hard to explain DDD since a lot of engineers perceive it as something like `man` pages, or even doc strings.
1. Pick the project. Something that you like, but also something that is not huge or very mature (they tend to have longer turn around due to the volume of things they are getting). You can then check issue tracker for the project and see if there are tasks labeled "need help", "good first issue", etc. They are usually things that team would appreciate help with. On GH, it looks like this: https://github.com/iterative/dvc/labels/good%20first%20issue
2. Check that project is "healthy" - check the recent history of PRs - that code is being reviewed and merged. It can be very demotivating to do stuff that won't be answered at all. Ask in the issue you like and see if there is a response.
3. Find the contributors guide. There should be a link in the project's README, something like https://github.com/iterative/dvc#contributing . The guide should help you understand how to do setup dev environment, how to create a PR, etc.
4. In a lot of cases these days, projects have chat or forum (or GH issue comments) to ask questions, ask help, etc. Check them our, see if they are "healthy" and don't hesitate to ping the team - usually people are responsive and are willing to help.
Good luck! and thank you for considering doing OSS contributions. That means a lot for them!
We had been paying ~$500/month for the open-source project (https://dvc.org) even before this new pricing model, and migrated to GitHub Actions recently- works like a charm. The only issue that we have now is Windows builds- machines on GH have a bit less resources, but it's good for us to optimize it anyway.
Agree that website is simple, but it can be promotional on his end- it's good to show best practices. And his website clearly has a lot of traffic, a lot of engineers read it.
We are the company behind very popular open-source tools for ML workflow- DVC.org and CML.dev. We are solving the complexity of managing datasets, ML infrastructure, ML models lifecycle management. We are Hashicorp for ML.
Join our well-funded remote-first DVC team if you love building open source, developer tools, and CLI. If you want to see how your code is used by thousands of developers every day! If you don't mind Python.
To my mind VS Code (and not only VS Code, vim and other editors also) not only competes with editors, but with IDEs as well. It's interesting that he doesn't mention JetBrains at all. I'm curious to see if there is a trend of people migrating from it (or not?) and why.
> GitHub’s Codespaces also run VS Code as a web app, this time by spinning up an ad hoc development environment.
This probably can be the first time we'll see people use online code platform for real.
I guess it doesn't help much their customers that they don't mention this with capital letters somewhere close to the search box. I would never use a search like this.
It's good to see how many great SE practices are being enhanced and adjusted and adopted to ML/DS. I would also recommend to run these tests on CI systems and create reports, pass/fail checks. This very common for SE these days and saves a lot of time, improves review process
Yep, the way US hospitals calculate prices and they way they negotiate this with insurance company looks ridiculous. It took me a few years to stop paying attention to this (assuming you have a good insurance!).
We are using k8s as well for a project that we also provide on-prem install too. We decided to use kustomize for now vs doing helm for now. Curious, have someone had experience with both to compare. What are the benefits of using helm?
We are the company behind very popular open-source tools for ML workflow- DVC.org and CML.dev. We are solving the complexity of managing datasets, ML infrastructure, ML models lifecycle management. We are Hashicorp for ML.
Join our well-funded remote-first DVC team if you love building open source, developer tools, and CLI. If you want to see how your code is used by thousands of developers every day! If you don't mind Python.
Please, find the full job description here - https://remoteok.io/remote-jobs/99244-remote-senior-software... ... and feel free to ask any questions.
To apply send a direct email to [email protected].