Call for Contributors (June 2023)

Become a leading voice among data scientists, machine learning engineers, and deep learning engineers

Updated Topic Focus

Moving forward, Heartbeat will narrow its focus to dive deeper into the subjects of Deep Learning, Computer Vision, NLP, and Comet-related content. This could be anything from a tutorial on Comet, a discussion of industry trends in Deep Learning, a computer vision project, etc.

For the moment, the publication will be focusing on these topics only, however we have a great collection of ML, MLOps, and other amazing content that will continue to live on the blog. In the future we may once more open to wider Machine Learning and Data Science topics, but we’re eager to be even more dedicated to these specific projects.

We’re also looking for more topics specifically related to ML experiment tracking and ML experiment management as they relate to Comet or the above mentioned topics.

If you’re writing about Comet content we want to see super detailed and accurate code and projects that really highlight our platform and show off different product features. Want to create a video deep dive to go along with it? Fantastic! We want to help new users find Comet and reduce friction around ML project creation.

Additionally, the pricing structure for Heartbeat articles will also be changing. The new payment per article is as follows:

• $250/article for Comet or Kangas-related content
• $150/article for LLM, LLMOps, Prompt Engineering, DL, CV, or NLP content (not Comet related)

If you have any questions regarding what content is appropriate, continue reading or reach out to Kasey at KaseyH@comet.com.

TL;DR

• Heartbeat is an editorially independent, community-led publication, newly sponsored by Comet — a leader in the MLOps and ML Experiment Management space.
• Heartbeat publishes contributor content focused on the needs of practicing data scientists, ML engineers, and deep learning engineers (i.e. “Data Science 201” and above — see below for more).
• We’re currently extra excited about articles in the deep learning and computer vision space.
• **We offer direct payment for each piece of contributed content (i.e. not through Medium’s Partnership Program), and our team provides full editorial support along the way.
• Contributors own the content they produce, and can republish on personal blogs, include in their project portfolios, etc.
• Content submissions are considered on a rolling basis.
• We celebrate our contributors, sharing their work across Comet’s community channels, including social media, newsletters, and more.
• There is absolutely no requirement or expectation that contributors mention, use, or otherwise reference Comet in their content.

**Important note: We are currently only able to process payments through two platforms: PayPal and Bill.com. Unfortunately, this means there are a number of locations around the world where we are unable to send payments. We use PayPal primarily for North American payments, and Bill.com for international payments. Please review this list of countries supported on Bill.com for more info. We apologize for this inconvenience, and we’re always happy to work with you, even if we aren’t able to process payment.

About Heartbeat

Heartbeat is an editorially-independent, contributor-driven publication centered on providing educational, informative, and technically-innovative content within the fields of Data Science (DS), Machine Learning (ML), and Deep Learning (DL). Our team offers payment and full editorial support for each piece of contributor content — we believe that knowledge sharing is work and should be supported and compensated as such.

Heartbeat has deep roots in the worlds of DS, ML, and DL — initially founded in 2017 and sponsored by Fritz AI until September 2021, the publication has been a home for practitioners and industry leaders who wish to share their knowledge, learning journeys, and expertise.

Now sponsored by Comet — an MLOps and Experiment Management industry leader — Heartbeat is returning to these roots as a premier provider of content focused on DS, ML, and DL practitioners.

About the Heartbeat Contributor Program

The Heartbeat team is excited to announce that we’re reopening content submissions from the community at-large!

Below is an overview of what you can expect from the program:

• What’s in it for you
• What kinds of content we’re interested in publishing
• An outline of the contributor program processes
• Submission and publication guidelines

What’s In It For You

• Help others. Helping others makes the heart feel good. Your technical expertise and perspective will be invaluable to your fellow data scientists, machine learners, and deep learners.
• Editorial support. We provide end-to-end, individualized editorial support to help you get the most out of your work — including SEO optimization and content sharing strategies.
• Promote your project. Show off what you’ve built to others interested in the same technology.
• Improve your personal brand. Your name is on the post. Become a well-known expert in the field.
• Broaden your exposure. Our staff will do everything we can to promote (and help you promote) your content across various channels. We curate weekly newsletter Deep Learning Weekly, and we often feature our favorite contributor posts in these letters.
• Join a growing community. We have a Slack community with more than 1000 talented, passionate data science and machine learning professionals.
• Get paid*. Heartbeat contributors are paid for their work — we believe people should be compensated for their time. Additionally, we’re able to increase payment for high-performing content and content that showcases the Comet platform.

*We pay contributors directly upon publication, not through Medium’s Partnership Program (MPP). More on this below. Please also note we cannot offer payment for content partnerships, wherein content is produced under the umbrella of another organization. If you’re unsure about your status with this policy, please don’t hesitate to ask.

Kinds of Content We’re Seeking: Data Science 201 and Above

At Comet, we’re committed to helping data scientists, ML engineers, and deep learning engineers build better models faster. It’s with that spirit and mission in mind that we’re (re)launching the Heartbeat Contributor Program.

While there are any number of excellent resources focused on defining what data science and machine learning are, providing Hello World model-building tutorials, and offering commentary on core, high-level concepts, we’re seeking to build the premier destination for community-contributed content that digs a bit deeper.

You can think of our content focus as anything that meets the threshold of “Data Science 201” and above. Extending on this analogy, in “Data Science 101,” you might learn the definitions of some core concepts, as well as how to train basic models.

But what happens next? This is the fundamental question we’re considering with Heartbeat’s contributor program, and it leads us to content that focuses on a series of follow-up questions:

• How can you 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 you improve your models and better understand the data they’re trained on?
• How do you 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?
• Is there a better, more performant model architecture out there for a given 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 certainly open to addressing other topics as well, including:

• Trends in DL research.
• Explorations of new tools and libraries — or best practices for existing ones.
• Computer vision, DL, or NLP projects.
• Best practices for collaborative data science and machine learning.

Process, Policies, and Notes

• You own the content you publish on Heartbeat. As such, you’re free to cross-post on a personal blog, share in a project portfolio, include in an ebook, etc. Comet and Heartbeat retain the right to host a copy of the content, as well as promote it across our community channels. However, we won’t accept previously published material. More details on this are included in the content licensing agreement (see below).
• Before submitting a draft to Heartbeat, ensure you’ve taken the time to proofread your draft for grammar, spelling, and language usage. While it’s okay to have a few typos and other minor issues, it’s always a best practice to put your best foot forward, so to speak. Drafts that contain extensive errors are much less likely to be considered for publication.
• The best content adds something new to the discussion — whether it’s a unique use case, recent research, or an extension of a traditional technical approach — so if you’re unsure if your post is a good fit for Heartbeat, search our archives. If you don’t find another post on the topic, then this is a good sign that you’re offering something unique. If there are several posts on the same topic… 😬
• We ask that contributors opt out of Medium’s paywall/distribution option when submitting posts to be published on Heartbeat. We pay contributors independently, not through Medium’s distribution program. Additionally, we want to keep your awesome content free and accessible to our global audience!
• We check all articles for plagiarism and copyright violations. If you need more info on what does and does not constitute plagiarism, here’s a good place to start. If you’re unsure, it’s completely okay to ask :) Violation of this policy will result in removal of the draft from our publication and, if necessary, a ban from submitting in the future.
• Because we offer to pay, and because it’s best practice to enumerate rights of usage and payment, we ask contributors to sign a licensing agreement for each piece of content they publish with Heartbeat. We’re happy to send you a template version of this agreement in advance so you can read through those terms and ask any questions you might have.
• As a general rule, we only accept previously unpublished blog posts for publication on Heartbeat. This includes drafts previously published on Medium, a personal blog, or other publication. If you would like us to consider a blog post that has already been published because you feel like it would be a perfect fit for Heartbeat, please include this information in your submission (process outlined below).

Publication Timeline

Due to the number of submissions received, we generally publish pieces 3–4 weeks after the draft is initially approved, though this can change based on numerous factors. Once we have placed your submission on our publishing calendar, we will let you know when to anticipate publication.

How Does it Work?

Given the topic considerations listed above, send us an article submission!

To submit your article submission please fill out the short Google Form embedded below.

While we try our best to respond to all submissions, our current volume doesn’t always allow this — if you haven’t heard back from us in a week, you can assume we’ve passed on your pitch. Sorry in advance for any inconvenience this may cause.

We look forward to seeing what you come up with!

Happy Writing,

Kasey & the Heartbeat Team

Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. We pay our contributors, and we don’t sell ads.

If you’d like to contribute, head on over to our call for contributors. You can also sign up to receive our weekly newsletter (Deep Learning Weekly), check out the Comet blog, join us on Slack, and follow Comet on Twitter and LinkedIn for resources, events, and much more that will help you build better ML models, faster.