Write for Us Machine Learning: Machine Learning Blog Guest Post Guidelines

Yes, machine learning guest posts are accepted on this blog. The ideal writer is a practitioner, data lead or founder who has shipped a machine learning project into a real product or business. We decline textbook explanations of algorithms, and posts written to sell a course, a bootcamp or a platform.

Most machine learning content is either a maths lecture or a shallow explainer that stops before anything useful. What this blog wants is a machine learning project that went into production, the problem it solved, the data and the trade-offs you made, what it got wrong at first, and what it changed for the business once it worked.

My readers are business owners, marketers and operators who keep hearing that machine learning will change their work but rarely see a plain account of one that did. They want the practical version: what it took, what it cost in effort, and whether it earned its keep, so a machine learning post has to teach from a shipped project, not a tutorial.

60-second machine learning guest post check

A strong submission can answer yes to all five points:

Have you shipped the machine learning work you are describing
Do you have a result, a metric or a clear trade-off to share
Does your opening answer the reader's question straight away
Have you removed any pitch for a course, platform or service
Could a technical reader learn from your decisions

Read the guidelines and submit your guest post.

What we publish

A machine learning model you put into production and the problem it solved
The data work behind a model and how long it really took
A model that underperformed and what you changed
Machine learning applied to a marketing, support or ops problem, with results
Buy versus build for a machine learning capability in a small company
How you explained a model's decisions to non-technical stakeholders
Monitoring a model in production and catching drift
A machine learning project you stopped and why it was the right call

If your post could be published on any machine learning site with the logo swapped, it is not for us.

What we don't accept

Algorithm explainers with no project behind them
Posts that promote one course, bootcamp or platform
Theory with no application to a real business problem
Previously published posts republished from another site
Thin content under 800 words with no shipped work behind it

What you get

Your article in front of an engaged audience of business owners, marketers and founders
One relevant, contextual link back to your own site
A byline and the credibility of an established, high-traffic blog
Editorial help to make the piece as strong as it can be before it goes live

How to submit

Send your pitch or draft through the contact form. Include the topic, the one specific problem the post solves, and a line on your first-hand experience with it. One idea per pitch works best. You will hear back on whether it is a fit before you write the full draft.

What a strong machine learning pitch looks like

A weak pitch says "I want to explain how neural networks work." A strong one says "We built a churn model that flagged at-risk accounts a fortnight earlier and lifted saves by a fifth, and I want to show the features that mattered and the two that looked useful but were leaking." One is a brochure. The other is worth reading.

The angles that get accepted fastest

A shipped model with the problem and the result
The data work nobody warns you about
A model that failed and the fix
A project a similar team could scope this week
A contrarian view on where machine learning is oversold

Looking for more places to pitch? The free write for us finder lists sites that take guest posts in your niche, and the free backlink checker shows what a placement is worth.

Frequently asked questions

Does Lilach Bullock accept machine learning guest posts?

Yes. Machine Learning guest posts are welcome from a practitioner, data lead or founders and similar writers who have done the work and can share what happened. We publish practical articles with real examples and decline textbook explanations of algorithms.

What machine learning topics do you accept?

We accept machine learning shipped into products, the real data work behind a model, models that failed and the fixes, buy versus build in small companies, and monitoring models in production. The common thread is a specific lesson with a result attached, not a general overview of machine learning.

Can I include a link back to my own site?

One contextual link back to your own site is fine when it is relevant and helps the reader. Posts written purely to build a backlink with no real value are not published.

Do you accept previously published content?

No. Every submission must be original and not published anywhere else first, including your own blog, Medium, LinkedIn, or a guest slot on another site under a different name. Republished or lightly reworded content gets declined, and we check before it goes live.

How do I submit a machine learning guest post?

Send your pitch through the contact form with the topic, the specific result or lesson you will cover, and a line on your own experience doing the work you are writing about. One idea per pitch works best, and you will hear back before writing a full draft.

Do you accept sponsored or commercial partnerships?

Commercial and partnership enquiries are handled separately from editorial guest posts. If you are a brand or agency wanting to work together rather than pitch an article, say so in the contact form and you will get the right details back.

How long should a machine learning guest post be?

There is no fixed word count, but posts that do well here usually run from 1,000 to 1,800 words. Long enough to show the example and the numbers, short enough that a busy owner finishes it. Cut anything that repeats what the reader already knows.

What makes a machine learning pitch stand out?

A real result in the first sentence. Say what you did, what changed and over what period, then say what the reader will be able to copy. For example: We built a churn model that flagged at-risk accounts a fortnight earlier and lifted saves by a fifth, and I want to show the features that mattered and the two that looked useful but were leaking.

Brands and agencies: sponsored posts and link placements on this blog are handled separately from editorial guest posts. See how to work with me or email [email protected].