{"componentChunkName":"component---src-templates-blog-post-tsx","path":"/january-20-dvc-heartbeat","result":{"data":{"markdownRemark":{"id":"e6daad77-8b3c-5712-853a-e4f23dc6323c","excerpt":"

Welcome to the New Year! Time for a recap of the last few weeks of activity in\nthe DVC community.

\n

News

\n

We were honored to be named a Project of the Year\nby Open Data Science, Russia’s largest community of data scientists and machine\nlearning practitioners. Check out our ⭐️incredibly…

","html":"

Welcome to the New Year! Time for a recap of the last few weeks of activity in\nthe DVC community.

\n

News

\n

We were honored to be named a Project of the Year\nby Open Data Science, Russia’s largest community of data scientists and machine\nlearning practitioners. Check out our ⭐️incredibly shiny trophy⭐️!

\n

DVC is the \"project of the year\" according to @odsai_en!
😱🏆🎉
Open Data Science the largest DS community we know, with over 40K active members, great courses and it's own conf Data Fest.
Many thanks to the organizers and voters!
This is the best surprize gift for the team!!🥳 pic.twitter.com/LZgewjM582

— 🦉DVC (@DVCorg) December 24, 2019

\n

DVC hit 100 individual contributors on Github! To celebrate our\n100th contributor, Vera Sativa, we\nsent her $500 to use on any educational opportunity and her own DeeVee (that’s\nour rainbow owl). We also awarded educational mini-grants to two of DVC’s\nbiggest contributors, Vít Novotný, and\nDavid Příhoda.

\n

\n \n \n \n \n \n \"odd\n \n \n Vera (center, flashing a\npeace sign) thanked us with this lovely picture of DeeVee and her team,\nOdd Industries. They are making some extremely neat tools\nfor construction teams using computer vision.

\n

We were at PyData LA! Our fearless leader\nDmitry gave a talk and we set up\na busy booth to meet with the Pythonistas of Los Angeles. It was a cold and\nblustery day, but visitors kept showing up to our semi-outdoor booth. We’re sure\nthey came for the open source version control and not the donuts.

\n

\n \n \n \n \n \n \"py\n \n \n \n\n \n \n \n \n \n \"py\n \n \n The DVC team and PyData\nvolunteers who heroically staffed our booth in the rain.

\n

Our engineer and technical writer Jorge reported:

\n
\n

We were super happy to meet all kinds of data professionals and enthusiasts in\nseveral fields who are learning and adopting DVC with their teams – including\nseveral working with privacy-sensitive medical records, very cool!

\n
\n
\n

From the community

\n

Here are some rumblings from the machine learning (ML) and data science\ncommunity that got us talking.

\n

A machine learning software wishlist. Computer scientist and writer\nChip Huyen tweeted about her ML software wishlist\nand kicked off a big community discussion.

\n

I've been thinking about the software stack for machine learning. Tools I'd love to see.

1. Pip for pretrained models.
2. Version control for datasets.
3. GPU-friendly CI. Travis CI, Circe CI don't support GPUs. Jenkins is a pain.
4. Fast dataframes. Why is Pandas so slow?

— Chip Huyen (@chipro) December 6, 2019

\n

Her tweet resonated with a lot of practitioners, who were eager to discuss the\nsolutions they’d tried. Among the many thoughtful replies and recommendations,\nwe were thrilled to see DVC mentioned.

\n

We're using @DVCorg for 2) and it works great. 🙂

— Kristijan Ivancic (@kristijan_ivanc) December 6, 2019

\n

If you haven’t already, definitely check out Chip’s\nthread, and follow her\non Twitter for more excllent, accessible content about ML engineering. We’re\nthinking hard about these ideas and hope the discussion continues on- and\noffline.

\n

A gentle intro to DVC for data scientists. Scientist\nElle O’Brien published a code walkthrough\nabout using DVC to make an image classification project more reproducible.\nSpecifically, the blog is a case study about version control when a dataset\ngrows over time. If you’re looking for a DVC tutorial geared for data\nscientists, this might be up your alley.

\n

\n \n
\n

Start Version Controlling your Machine Learning Datasets

\n
Make your machine learning and data science projects reproducible with open source tools.
\n
medium.com
\n
\n
\n \"Start\n
\n \n
\n

\n

Ideas for data scientists to level up their code Machine learning engineer\nAndrew Greatorex posted a blog called “Down with technical debt! Clean Python\nfor data scientists.” Andrew highlights something we can easily relate to: the\n“science” part of data science, which encourages experimentation and\nflexibility, sometimes means less emphasis on readable, shareable code. Andrew\nwrites:

\n
\n

“I’m hoping to shed light on some of the ways that more fledgling data\nscientists can write cleaner Python code and better structure small scale\nprojects, with the important side effect of reducing the amount of technical\ndebt you inadvertently burden on yourself and your team.”

\n
\n

In this blog, DVC gets a shout-out as Andrew’s preferred data versioning tool,\nused in conjunction with Git for versioning Python code. Thanks!

\n

\n \n
\n

Down with technical debt! Clean Python for data scientists.

\n
\n
medium.com
\n
\n
\n \"Down\n
\n \n
\n

\n

An introduction to MLOps Engineer\nSharif Elfouly wrote an approachable guide\nto thinking about MLOps, the growing field around making ML projects run\nefficiently from experimentation to production. He summarises why managing ML\nprojects can be fundamentally different than traditional software development:

\n
\n

“The main difference between traditional software and ML is that you don’t\nonly have the code. You also have data, models, and experiments. Writing\ntraditional software is relatively straightforward but in ML you need to try\nout a lot of different things to find the best and fastest model for your\nuse-case. You have a lot of different model types to choose from and every\nsingle one of them has its specific hyperparameters. Even if you work alone\nthis can get out of hand pretty quickly.”

\n
\n

Sharif gives some recommendations for tools that work especially well for ML,\nand he writes that DVC is the “perfect combination for versioning your code and\ndata.” Thanks, Sharif! We think you’re perfect, too.

\n

\n \n
\n

MLOps Done Right

\n
What is MLOps? Why is it so important? How to do it right!
\n
medium.com
\n
\n
\n \"MLOps\n
\n \n
\n

\n

That’s a wrap for January. We’ll see you next month with more updates!

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