Hey, welcome
So you found this page. That’s cool.
Here’s what’s actually happening. We run Automation ES because the AI development space moves incredibly fast. Most people struggle to keep up with new tools, frameworks, and best practices.[attached_file:1] Everyone’s talking about AI development now, but very few people actually explain how to build AI systems well, what works in production, and where things go wrong. We wanted to create a place where people could get real information about AI development from people who actually do it.
Why AI Development Matters to Our Readers
Our readers want to understand AI development without all the hype. Most importantly, they want to know which approaches actually work for building AI systems. Furthermore, they’re looking for practical guidance they can use immediately. Additionally, they want honest conversations about both successes and failures in AI development. Instead of theoretical nonsense, they need real experience.
If you know AI development, and you’ve actually built AI systems that work, you could explain the process in a way that makes sense. We want you writing here. Whether you’re an AI engineer, a machine learning specialist, someone who’s deployed AI systems, or you’ve just spent years learning what works in AI development, there’s definitely space for you.
This isn’t about selling AI tools or promoting frameworks. Instead, it’s about helping people actually develop better AI systems.
Who we’re looking for
Real talk? We’re selective, but not in a gatekeeping way. We just need people who actually know their stuff about AI development.
If you fit any of these, we’d love to hear from you:
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AI engineers and machine learning specialists – You build AI systems that actually work
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Data scientists with AI development experience – You understand the full pipeline
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Software engineers working on AI projects – You know how to implement AI in production
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AI researchers and academics – You understand both theory and practical AI development
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ML operations professionals – You manage and scale AI systems
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AI startups founders and technical leads – You’ve shipped AI products
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People who’ve deployed AI development systems – You’ve dealt with real-world challenges
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Anyone seriously studying AI development – And you can back it up with real experience, not just coursework
You don’t need some fancy PhD. However, real hands-on experience matters most. Some of the best AI development knowledge comes from people who’ve just spent years building, breaking things, and learning what actually works. What matters most is that you genuinely know this stuff. Like, really know it. We can tell when someone’s just reading papers about AI development, and so can our readers.
Topics we’re genuinely hungry for
Look, I could list forever, but here’s what would actually resonate with Automation ES readers:
Fundamentals & Getting Started
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AI development basics – For people just starting with machine learning
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Choosing the right AI framework – TensorFlow, PyTorch, scikit-learn comparison
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Building your first AI model – Step-by-step practical guide
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Data preparation for AI development – The most important (and boring) part
Advanced AI Development
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Training and optimization – Making AI models actually work well
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AI development with large datasets – Handling scale in real projects
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Transfer learning in AI development – Reusing models to save time
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Fine-tuning pre-trained models – Practical AI development shortcuts
Production & Deployment
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Deploying AI systems – From notebook to production
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AI model monitoring – Keeping systems working after launch
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Performance optimization for AI – Making models run faster and cheaper
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Scaling AI development infrastructure – Building systems that handle growth
Practical Applications
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Computer vision in AI development – Image recognition, object detection
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Natural language processing with AI – Text analysis and generation
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Time series forecasting – Predicting future data points
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Recommendation systems – Real-world AI applications
Best Practices
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Testing and validation in AI development – How to know if your model actually works
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AI development workflow and tools – GitHub, MLflow, experiment tracking
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Documentation for AI projects – Making AI development reproducible
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Common AI development mistakes – Learning from others’ failures
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Real case studies – How teams actually built successful AI systems
If you’ve got an angle that actually helps people build better AI systems, pitch it. We’re not going to be difficult about it.
What we actually need from your article
Alright, so here’s what makes an article work for Automation ES:
Length & Originality
Word count: Aim for somewhere around 1,500 to 2,500 words. Shorter AI development tutorials? 1,000–1,200 is totally fine. Just don’t pad it with garbage to hit numbers. That’s lazy and obvious.
Keep it original: Your article should genuinely reflect your own ideas. Avoid copying from documentation, rewriting tech blogs, or stealing from tutorials. We value what you know — your experience and true perspective on AI development.
Tone & Credibility
Talk like a real person: Write like you’re explaining AI development to a colleague over coffee. Short sentences work best. Normal paragraphs are key. Avoid heavy jargon overload. If you use a technical term, explain what it actually means. Not everyone’s studied computer science.
Back your claims up: If you’re saying something about AI development works, show why. Use code examples, your own projects, real results from production. Just be honest about how you know what you know. Our readers appreciate transparency about AI development.
SEO & Structure
Make it readable: Use headings so people can skim it. Start with something that hooks them. End with actual takeaways they can apply to AI development. Don’t bury important concepts in the middle.
Keywords should feel natural: Use “AI development” in your intro, in a heading or two, and somewhere near the end. But don’t force it. If it feels weird, it IS weird.
Link to our other stuff: When it makes sense, link to other Automation ES posts. For instance, if you’re discussing machine learning frameworks, reference existing tech content. Additionally, talking about AI applications? Link to relevant pieces about gadgets or startups. Moreover, discussing future tech? Connect to AI and innovation articles.[attached_file:1] This helps readers find more and helps us too.
Give us your SEO title and description: Tell us what you’d call it in Google (under 60 characters) and write something short that makes people click (under 155 characters).
How to format your article
I know formatting sounds boring, but it actually matters when people are reading on their phones:
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Headings: Use H2 and H3. That’s it. Don’t go deeper. People need clear markers to know what section they’re in.
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Keep paragraphs short: 2–4 sentences max. Long paragraphs just don’t work. People read on mobile now.
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Use lists: Bullets for tips, benefits, or lists. Numbers for step-by-step AI development processes. People love lists. Easy to scan.
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Bold the important stuff: If there’s something people really need to remember about AI development, bold it. Just don’t overdo it.
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Use code examples: Got code snippets or technical examples? Include them. Make it practical. People learn better with actual code.
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Use real examples: Talk about actual AI development projects you’ve worked on. Tell what worked, what didn’t. Way more interesting than theory.
The link rules (keeping it honest)
Here’s the deal:
Our articles: YES. Link to other Automation ES posts when relevant. Helps readers explore and helps us.
Legit AI and tech resources: YES. Official documentation, research papers, trusted tech resources.
Your own AI products or services: NO. Don’t link to your AI startup or your consulting business. We can tell.
Affiliate links: NO. No commissions disguised as helpful AI development advice. People see through it.
Promotional spam: NO. Random links to random AI tools you don’t actually use? Nope.
Your website in your bio: YES. One link. Keep it professional and relevant.
Simple rule: If this link actually helps the person reading understand or improve their AI development skills, include it. If it’s just promotional? Don’t do it.
Write us your author bio
At the end of your article, include a short bio about yourself. Keep it real. 50–100 words. Tell us:
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Your name and what you actually do in AI development
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Your experience building AI systems (what have you shipped?)
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What you specialize in or what you’re known for
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Where people can find you online
Here’s an example:
Alex is an AI engineer who’s spent like 7 years building machine learning systems at startups and bigger companies. He’s shipped production models for recommendation systems, computer vision, and NLP tasks. He’s experienced real-world AI development challenges and learned what actually matters. He loves helping others avoid mistakes he made. Find him on GitHub or his technical blog.
How to actually submit your article
You wrote something solid. Now what?
Step 1: Email us your pitch first. Don’t send the whole article yet. Subject line: “Guest Article Pitch – AI Development”
Tell us what the article’s about (few bullet points), why our readers would care, and why you’re qualified to write it. Keep it short—one paragraph max.
Step 2: Wait for us to get back to you. Usually takes like 5–7 business days. If we like your idea, we’ll say yes. If it’s not quite right, we’ll be honest.
Step 3: Write the complete article, adhering to the guidelines above. Ensure it is well-written, honest, and genuinely helpful.
Step 4: Send it as a Google Doc or Word file. Include your SEO title, meta description, author bio, and notes about where internal links should go.
Step 5: We review it. Might be small edits, might be bigger changes. We’ll let you know what’s happening and when it goes live. Then you can share it everywhere.
That’s it. Pretty straightforward.
Why we actually need you
Here’s the honest part. Automation ES exists because people like you share what they actually know. Every article helps someone. Perhaps it helps them finally understand how to approach AI development. Maybe it saves them months of mistakes by showing what doesn’t work. Additionally, it could help them deploy their first AI system successfully.
AI development information is everywhere, but a lot of it is either outdated, overly theoretical, or incomplete. We’re trying to be different. Honest. Actually helpful. Actually practical about AI development.
If you care about helping people build better AI systems and understand real-world AI development, and you want an audience of people who will actually listen and take action, this is the place. I genuinely think what you know could change how someone approaches their AI development journey.
Ready to write for us about AI development? Send your pitch over. We’re actually excited to see what you’ve got.