March ’21 Heartbeat

Monthly updates are here! Read all about our growing team, our CEO's interview on TFIR, Elle's talk at DataTalks.Club Conference and more!

  • Jeny De Figueiredo
  • March 15, 20213 min read
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Welcome to March! It's been a great month already! Here's all that will keep you in the know.


ICYMI - DVC 2.0 is here!

If you somehow missed our March 3rd announcment, DVC 2.0 is here with loads of features to make your life easier.

🧪 Lightweight ML experiments

📍 ML model checkpoints versioning

📈 Dvc-live - new open-source library for metrics logging

🔗 ML pipeline templating and iterative foreach-stages

🤖 CML - new way to get GPU/CPU in clouds and GitHub Actions

This video from the team gives you an overview of all the new features.

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And we keep on growing our worldwide team! 🌏

We have three new team members this month!

Laurens Duijvesteijn joins the team from Utrecht, The Netherlands as a backend infrastructure engineer. Previously he led a devops team at Channable where he learned that he really enjoys working on developer tools and empowering people to do great work. When not solving dev challenges, he enjoys bouldering/climbing, snowboarding and hiking! Welcome Laurens!

Helio Machado joins our team from Spain as a CML engineer! Helio comes from a heutogogic background, mainly focused on the Free and Open Source culture and technologies from a systems perspective. You will find his clever cryptograph handle helping you out in Discord with your CML questions. Fun fact: Our two CML engineers, Helio and David Ortega live just 300 km apart in Spain! CML has some Spanish flare! 💃🏻🇪🇸

MikHail Rozhkov joins us from Moscow, Russia as a Solution Engineer. Mikhail has been working with DVC for 2+ years in the banking industry and is also the creator of the Machine Learning REPA community as well as created our first course on Udemy. We are so excited to have him officially join our team full-time!

Join Us

Open Positions

Come join our team! Open positions this month:

TypeScript Front-End Engineer to build SaaS and a VS Code UI for our popular machine learning tools: DVC and CML. The ML tools ecosystem is what JS space was 10 years ago. Come join us on this exciting project!

Our search continues for a Developer Advocate to support and inspire developers by creating new content like blogs, tutorials, and videos - plus lead outreach through meetups and conferences.

Does this sound like you or someone you know? Be in touch!

Swapnil Bhartiya of TFIR Insights interviewed our very own CEO, Dmitry Petrov, on his show discussing:

  • Why Open Source is a better approach for AI/ML
  • DVC and CML
  • Who should care about these tools
  • How DVC and CML stack up against proprietary AI Platforms such as AWS SageMaker and Microsoft Azure ML Engineer
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Elle at DataTalks.Club Conference

Elle O'Brien presents her talk "Automating ML with Continuous Integration" at the DataTalks.Club Conference with Alexey Grigorev and Demtrios Brinkmann of MLOps Community. You can catch her talk starting at 3:03 below. 👇🏼

Automating ML with Continuous Integration

Elle O'Brien, PhD presents at DataTalks.Club Conference
Automating ML with Continuous Integration

From the Community

Using DVC in Lab Data Management

This great tutorial from Matsui-lab Blog provides a solution using DVC for the data management problem labs face.

Versioning a Shared Dataset Using DVC and S3

DVC solution in a lab environment
Versioning a Shared Dataset Using DVC and S3

Healthcare Use Case Video Tutorial

Danial Senejohnny created this video outlining the use of DVC for healthcare institutes where the data must be kept private and on premise data store is preferred. 👇🏼

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Scientific Journals 🧑🏻‍🔬

We are excited to announce a scientific paper purely devoted to DVC coming out from Queen's University. This publication by Amine Barrak, Ellis E Eghan and Bram Adams, will be presented at the 28th IEEE International Conference on Software Analysis, Evolution, and Reengineering. You can check it out here. 👇🏼

On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects

Empirical Study of DVC Projects
On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects

This article by Samuel Idowu, Daniel Struber, and Thorsten Berger, reviews a number of asset management tools for machine learning including DVC, that solve the commonly reported ML engineering challenges.

Asset Management in Machine Learning: A Survey

Steps to use DVC in your data versioning
Asset Management in Machine Learning: A Survey


Tweet Love ❤️

From a Portuguese speaking community member in Finland…

"The @DVCorg surely it is among the best tools of the ecosystem of the last 3 years. It won't be long before DVC is as common as Scikit-Learn in ML / DS projects with high maturity. 👏🏼👏🏼👏🏼"

O @DVCorg seguramente está entre as melhores ferramentas do ecossistema dos últimos 3 anos. Não vai demorar para o DVC ser tão comum quanto o Scikit-Learn em projetos de ML/DS com alta maturidade. 👏👏👏

— Flávio Clésio March 3, 2021

We think so too! 🙌🏼 You're all caught up! See you at the next Community Gems 💎!

Do you have any use case questions or need support? Join us in Discord!

Head to the DVC Forum to discuss your ideas and best practices.

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