Where to publish AI engineering articles

Where to publish AI engineering articles depends on your target audience, whether you are sharing practical implementation guides, industry insights, or novel research.

1. Developer Platforms & Online Publications Best for practical tutorials, architecture breakdowns, MLOps, LLM application engineering, and building a public developer brand. Medium Publications Towards Data Science: Highly popular publication dedicated to data science, machine learning, and AI engineering workflows. Towards AI: Focuses on AI tutorials, deep learning, and practical application guides. Artificial Intelligence in Plain English: Great for accessible technical guides and beginner-to-intermediate tutorials. Dev.to: A software-engineering focused community where technical posts on AI tools, SDKs, and local AI implementations perform well. Substack / Beehiiv: Ideal if you want to build an independent audience, write newsletter-style deep dives, and own your distribution list. HackerNoon: Great for opinion pieces, architectural walkthroughs, and practical software engineering with AI integrations. InfoQ: High-standard engineering editorial platform that accepts contributed technical articles on AI/ML system architecture and enterprise MLOps.
2. Open-Access Preprint Repositories Best for sharing research, benchmarks, or technical reports rapidly before or alongside peer review. arXiv (cs.AI, cs.LG, cs.CL, cs.SE): The gold standard for preprinting artificial intelligence, machine learning, and software engineering papers. Provides immediate visibility and citation capabilities. TechRxiv: IEEE’s open preprint server dedicated to engineering, computer science, and technology. ResearchGate: A platform to host technical reports, working papers, and preprints while connecting directly with other researchers.
3. Top Peer-Reviewed Academic Conferences Best for cutting-edge theoretical ML, novel engineering architectures, systems ML, and benchmark datasets. Systems & ML Conferences MLSys (Conference on Machine Learning and Systems): Tailored specifically to the intersection of computer systems and machine learning. NeurIPS / ICML / ICLR: The top-tier theoretical and applied machine learning conferences. KDD: Premier venue for data mining, applied data science, and enterprise AI engineering. Domain & Application Conferences ACL / EMNLP / NAACL: Top conferences for Natural Language Processing (NLP) and LLM engineering. CVPR / ICCV / ECCV: Premier venues for Computer Vision applications. IEEE ICRA / IROS: Leading conferences for AI in robotics and autonomous systems.
4. Peer-Reviewed Academic & Professional Journals Best for rigorous, long-form empirical studies, system frameworks, and industrial applications. IEEE Transactions on Neural Networks and Learning Systems (TNNLS): High-impact journal for ML systems and neural computation. Journal of Machine Learning Research (JMLR): Open-access, premier journal covering ML algorithms and systems. IEEE Software / IEEE Intelligent Systems: Focused on the practical software engineering side of building real-world AI systems. AI Magazine (AAAI): Bridges the gap between academic AI research and real-world application stories. MDPI AI for Engineering: Open-access venue specifically covering applied AI in engineering systems.
5. Industry Tech Blogs & Corporate Publications Best for demonstrating enterprise-scale AI implementations and industry leadership. Company Engineering Blogs (e.g., Uber, Airbnb, Meta, Netflix TechBlog): Many top tech companies accept guest posts or co-authored pieces if done in collaboration with internal engineers. O'Reilly Radar / Analytics: Features thought leadership on AI engineering trends, infrastructure, and real-world enterprise adoption.