July '20 Heartbeat

content/blog/2020-07-10-july-20-dvc-heartbeat.md

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+---
+title: July '20 Heartbeat
+date: 2020-07-10
+description: |
+  Catch up on new DVC releases, talks, and projects in our 
+  community. This month, we recap the DVC 1.0 release, 
+  making the list of top 20 fastest growing open-source 
+  startups, and interviews galore. Plus: 📣 an invitation to 
+  the next DVC meetup!
+descriptionLong: |
+  Catch up on new DVC releases, talks, and projects in our 
+  community. This month, we recap the DVC 1.0 release, 
+  making the list of top 20 fastest growing open-source 
+  startups, and interviews galore. Plus: 📣 an invitation to 
+  the next DVC meetup!
+picture: 2020-07-10/july_20_heartbeat_header.png
+author: elle_obrien
+commentsUrl: https://discuss.dvc.org/t/july-20-dvc-heartbeat/439
+tags:
+  - Heartbeat
+  - CI/CD
+  - DVC 1.0
+  - SciPy
+  - MLOps
+  - Reproducibility
+  - Meetup
+---
+
+Welcome to the July Heartbeat, our monthly roundup of [new releases](#news),
+[talks](#community-activity), [great articles](#good-reads), and
+[upcoming events](#coming-up-soon) in the DVC community.
+
+## News
+
+### DVC 1.0 release
+
+On June 22, DVC entered a new era: the
+[official release of version 1.0](https://dvc.org/blog/dvc-1-0-release). After
+several weeks of bug-catching with our pre-release, the team has issued DVC 1.0
+for the public! Now when you
+[install DVC through your package manager of choice](https://dvc.org/doc/install),
+you'll get the latest version. Welcome to the future.
+
+To recap, DVC 1.0 has some big new features like:
+
+- Plots powered by Vega-Lite so you can compare metrics across commits
+- New and easier pipeline configuration files- edit your DVC pipeline like a
+  text file!
+- Optimizations for data transfer speed
+
+Read all the [release notes](https://dvc.org/blog/dvc-1-0-release) for more, and
+stop by our [Discord](https://discordapp.com/invite/dvwXA2N) if you need support
+migrating (don't worry, 1.0 is backwards compatible).
+
+### Virtual meetup!
+
+In May, we had our
+[first every virtual meetup](http://localhost:8000/blog/may-20-dvc-heartbeat).
+We had amazing talks from [Dean Pleban](https://twitter.com/DeanPlbn) and
+[Elizabeth Hutton](https://github.com/ehutt), plus time for Q&A with the DVC
+team- you can
+[watch the recording](https://www.youtube.com/watch?v=19GMtrFykSU&list=PLVeJCYrrCemiOc1SS_PIB3Tb3HX0Aqw3j)
+if you missed it!
+
+On Thursday, July 30, we're hosting our second meetup! Ambassador
+[Marcel Ribeiro-Dantas](http://mribeirodantas.me/) is hosting once again. We'll
+have short talks about causal modeling and CI/CD, plus lots of time for chatting
+and catching up. Please RSVP!
+
+<blockquote class="embedly-card"><h4><a href="https://www.meetup.com/DVC-Community-Virtual-Meetups/events/271844501/">July DVC Meetup: Data Science & DevOps!</a></h4><p>This meetup will be hosted by DVC Ambassador Marcel! AGENDA:We have two 10-minute talks on the agenda:- Causal Modeling with DVC - Marcel- Continuous integration for ML case studies - Elle Following talks, we'll have Q&A with the DVC team and time for community discussion.</p></blockquote>
+<script async src="//cdn.embedly.com/widgets/platform.js" charset="UTF-8"></script>
+
+### DVC is in the top 20 fastest-growing open source startups
+
+Konstantin Vinogradov at [Runa Capital](https://runacap.com/) used the GitHub
+API to
+[identify the fastest growing public repositories on GitHub](https://medium.com/runacapital/open-source-growth-benchmarks-and-the-20-fastest-growing-oss-startups-d3556a669fe6)
+in terms of stars and forks. He used these metrics to estimate the top 20
+fastest growing startups in open source software. And guess what, DVC made the
+cut! We're in great company.
+
+![](/uploads/images/2020-07-10/top20startups.png)
+
+### New team member
+
+We have a new teammate-[Maxim Shmakov](https://www.linkedin.com/in/mvshmakov/),
+previously of Yandex, is joining us! Maxim is a front-end engineer joining us
+from Moscow. Please welcome him to DVC. 👋
+
+## Community activity
+
+We've been busy! Although we are mostly homebound these days, there has been no
+shortage of speaking engagements. Here's a recap.
+
+### Meetings and talks
+
+- Co-founders Dmitry and Ivan appeared on the HasGeek TV series
+  [Making Data Science Work](https://hasgeek.com/fifthelephant/making-data-science-work-session-3/)
+  to discuss engineering for data science with hosts
+  [Venkata Pingali](https://www.linkedin.com/in/pingali/) and
+  [Indrayudh Ghoshal](https://www.linkedin.com/in/indrayudhghoshal/). The
+  livestream is available for viewing on YouTube!
+
+https://www.youtube.com/watch?v=EWcpALbzZRg
+
+- Dmitry appeared on the [MLOps.community](https://mlops.community/) meetup to
+  chat with host [Demetrios Brinkmann](https://www.linkedin.com/in/dpbrinkm/).
+  They talked about the open source ecosystem, the difference between tools and
+  platforms, and what it means to codify data.
+
+https://www.youtube.com/watch?v=ojV1tK9jXH8&t=2295s
+
+- I (Elle) gave a talk at the
+  [MLOps Production & Engineering World](https://mlopsworld.com/) meeting,
+  called "Adapting continuous integration and continuous delivery for ML". I
+  shared an approach to using GitHub Actions with ML projects. Video coming
+  soon!
+
+https://twitter.com/TMLS_TO/status/1273693487104503808
+
+- Extremely early the next morning, clinician-scientist
+  [Cris Lanting](https://www.linkedin.com/in/crislanting/?originalSubdomain=nl)
+  and I co-led a workshop about developing strong computational infrastructure
+  and practices in research as part of the
+  [Virtual Conference on Computational Audiology](https://computationalaudiology.com/).
+  We talked about big ideas for making scientific research reproducible,
+  manageable, and shareable. For the curious, the workshop is still viewable!
+
+https://www.youtube.com/watch?v=W4CoptalWw0
+
+- DVC has a virtual poster at [SciPy 2020](https://www.scipy2020.scipy.org/)! We
+  prepared a demo about
+  [packaging models and datasets like software](https://dvc.org/blog/scipy-2020-dvc-poster)
+  so they can be widely disseminated via GitHub.
+
+### Good reads
+
+Some excellent reading recommendations from the community:
+
+- Data scientist Déborah Mesquita published a thorough guide to using new DVC
+  1.0 pipelines in a sample ML project. It's truly complete, covering data
+  collection to model evaluation, with detailed code examples. If you are new to
+  pipelines, do not miss this!
+
+<external-link
+href="https://towardsdatascience.com/the-ultimate-guide-to-building-maintainable-machine-learning-pipelines-using-dvc-a976907b2a1b"
+title="The ultimate guide to building maintainable Machine Learning pipelines using DVC"
+description="Learn the principles for building maintainable Machine Learning pipelines using DVC"
+link="medium.com"
+image="/uploads/images/2020-07-10/pipes.jpg"/>
+
+- Caleb Kaiser of [Cortex](https://github.com/cortexlabs/cortex) (another
+  startup in the Runa Capital's Top 20 list!) shared a thinkpiece about
+  challenges from software engineering that can inform production ML. We really
+  agree with what he has to say about reproducibility:
+
+> You typically hear about “reproducibility” in reference to ML research,
+> particularly when a paper doesn’t include enough information to recreate the
+> experiment. However, reproducibility also comes up a lot in production ML.
+> Think of it this way — you’re on a team staffed with data scientists and
+> engineers, and you’re all responsible for an image classification API. The
+> data scientists are constantly trying new techniques and architectural tweaks
+> to improve the model’s baseline performance, while at the same time, the model
+> is constantly being retrained on new data. Looking over the APIs performance,
+> you see one moment a week ago where the model’s performance dropped
+> significantly. What caused that drop? Without knowing exactly how the model
+> was trained, and on what data, it’s impossible to know for sure.
+
+<external-link
+href="https://towardsdatascience.com/what-software-engineers-can-bring-to-machine-learning-25f458c80e5"
+title="What software engineers can bring to machine learning"
+description="Many production machine learning challenges are paralleled in software engineering"
+link="medium.com"
+image="/uploads/images/2020-07-10/tds.jpg"/>
+
+- Mukul Sood wrote about the Real World, a place beyond Jupyter notebooks where
+  data is non-stationary and servers are unreliable! He covers some very real
+  challenges for taking a data science project into production and introduces
+  the need for CI/CD practices in healthy, scalable ML applications.
+
+<external-link
+href="https://towardsdatascience.com/scaling-machine-learning-in-real-world-cb601b2baf4a"
+title="Scaling Machine Learning in the  Real World"
+description="Any conversation around scaling or productionizing data science, would need to talk about Continuous Integration/Continuous Deployment."
+link="medium.com"
+image="/uploads/images/2020-07-10/storm.jpg"/>
+
+### A nice tweet
+
+We'll close on a nice tweet from [Russell Jurney](https://datasyndrome.com/):
+
+https://twitter.com/rjurney/status/1266735603921547264
+
+Thanks, we couldn't do it without our community! As always, thanks for joining
+us and reading. There are lots ofways to stay in touch and we always love to
+hear from you. Follow us on [Twitter](twitter.com/dvcorg), join our
+[Discord server](https://discordapp.com/invite/dvwXA2N), or leave a blog
+comment. Until next time! 😎