Heartbeat is now part of the Comet Community
Helping data science, machine learning, and deep learning practitioners build better models, faster
Dear Heartbeat Readers,
It’s with great pleasure today that I’m reintroducing myself as the new (and old at the same time) Editor-in-Chief of Heartbeat!
If you don’t remember me, I promise I won’t take it personally…but I was one of the founding editors of Heartbeat when it launched back in 2017.
It’s been a long and winding journey from here to there, but I’m excited to share that moving forward, Heartbeat will be sponsored by Comet, an MLOps (Machine Learning Operations) startup focused on Experiment Management and Production Model Monitoring.
I wanted to take a moment to share the what and the why of this transition for both Heartbeat and Comet, as well as let you know how you can get involved in this new era for Heartbeat.
Happy Learning,
Austin
Editor-in-Chief, Heartbeat
Head of Community, Comet
Heartbeat, Meet Comet
When it comes to publishing high-quality, community-driven content that explores the bleeding edge of the tech world, Heartbeat has been an industry leader since its founding in 2017.
From state-of-the-art deep learning, to mobile machine learning, to augmented reality, Heartbeat’s vibrant community of developers, engineers, and content creators have generated and shared insightful technical resources that have reached more than 4 million visitors across 230 countries around the world.
Our plan at Comet is to not only keep Heartbeat alive, but to help it flourish and evolve in a new era.
About Heartbeat
Heartbeat — both the community and the publication — are incredibly dear to me.
Four years ago, in my first foray into the tech industry with Fritz AI (Heartbeat’s previous sponsor), I was tasked with helping to build a community-led publication from the ground up that explored the intersection of machine learning and mobile development — about as bleeding edge as you could get, in those days.
We also built this community and publication on a set of values and principles that I still deeply believe in: we paid our contributors for their work; we supported them editorially and by making them heroes; and we didn’t sell ads or otherwise monetize the publication.
What resulted was a product of love, commitment, and continuous experimentation: an industry-leading publication hosting content crafted by more that 120 unique, talented voices from more than two dozen countries.
And I couldn’t be more thrilled to carry on the values, spirit, and legacy of Heartbeat in my new role as Head of Community at Comet — and in a more familiar role as Editor-in-Chief of Heartbeat!
Moving forward, Heartbeat will carry on these traditions while also reimagining and reinventing the kinds of content you’ll find within its pages. I want to briefly share what that future will look like — as well as the “why” behind this transition.
Why the Transition?
As things grow and evolve, they must also change — and this particular change for Heartbeat came about in quite a serendipitous fashion.
Upon the beginning of my role at Comet, my former colleagues and friends over at Fritz AI were simultaneously undergoing a transition of their own; one that necessitated that they find a new, suitable home for the publication and community they’d been sponsoring and nurturing for years.
At the same time, our team at Comet was trying to find new ways to foster relationships with the larger data science and machine learning communities, as well as offer a platform for data science (DS), machine learning (ML), and deep learning (DL) practitioners to share their insights as they build the future of these burgeoning fields.
Given this desire, Heartbeat’s legacy as a home for high-quality technical content, and that crazy thing we call timing, the stars were aligned to welcome Heartbeat into the Comet Community — or, you might even say, to welcome Comet into the Heartbeat Community! 😉
The Next Era of Heartbeat: Building Better ML Models Faster
At Comet, we believe that in order for businesses, end users, and the public at large to truly receive value from ML-powered products and solutions, more emphasis must be placed on developing best practices that help practitioners and teams build better ML models faster.
To achieve that lofty goal, we understand that knowledge sharing will be crucial in steering model development practices and standards in the right direction.
That’s why we’re focusing this new era of Heartbeat explicitly on content resources that help practitioners solve the technical hurdles they face — you can think of this content focus as anything at the “Data Science 201” level of sophistication and above. More foundational, introductory content is of course valuable, and we love the many communities out there that support this work and these resources.
But we also realize that learning the foundations alone won’t move the needle. Instead, we believe we’re uniquely positioned to feature technical content and projects from practitioners in the field that speak to these second-order concerns and beyond.
Specifically, we’re interested in featuring technical content that attempts to answer questions like:
- How can we better tackle specific use cases with data science and machine learning?
- What kinds of work goes into scoping, planning, and executing an end-to-end project?
- How do we improve our models and better understand the data they’re trained on?
- How do we evaluate models, optimize them, or otherwise take the next steps toward models that are production-ready?
- When a model is ready to push to production, what does that workflow look like?
- How do we go about monitoring and evaluating models once they’re in production in real-world settings?
- Is there a better, more performant model architecture out there for a given task or use case?
- Is deep learning an appropriate approach to the problem at hand?
These are just a few of the questions we’re hoping our contributor community will tackle, but we’re committed to addressing other topics as well, including:
- Trends in ML research.
- Explorations of new tools and libraries — or best practices for existing ones.
- Best practices for collaborative data science and machine learning.
- Creativity, art, and other novel applications of ML.
Additionally, we’ll occasionally share content from our team of experts — some of that content will explore Comet’s approach to ML, some will explore advanced ML use cases, and some will tackle best practices across the ML lifecycle.
How to Get Involved
If you’re a longtime Heartbeat community member or contributor, then you already know the basics — we’re officially open to content contributions from the community!
Head on over to our updated Call for Contributors to learn what’s the same, what’s changed, and how to submit your proposals for the Heartbeat Contributor Program:
If you’re new to the community and want to check out how we got here, I’d encourage you to dig through the Heartbeat archives for topics that interest you.
Also, if you’d like to learn more about Comet, Heartbeat’s new sponsor, we have several ways to follow along with what our team is up to:
More information about Comet:
- About Comet: A bird’s eye view of what we focus on over at Comet, and how we’re working to help practitioners and teams build better models faster
- Comet Newsletter: Follow along with what our team is up to with this weekly email update — new content, events, product launches, and more
- Community Slack: Our community Slack group has upwards of 1,200 DS/ML/DL professionals, learners, enthusiasts and more. Join the conversation with us!
Subscribe to Deep Learning Weekly
Comet is also the new sponsor of Deep Learning Weekly, an industry-leading weekly roundup of the most relevant and compelling news, tutorials, code, research, and more.
Follow us on Twitter and LinkedIn
We’re active on both Twitter and LinkedIn, so be sure to follow along as we share all kinds of content, perspective, company updates, and much more!
