The New Stack encourages its readers to contribute to Towards Data Science, a leading platform for data science and AI. Recognizing the increasing convergence of cloud infrastructure, DevOps, and AI engineering, the article invites practitioners to share their experiences with building and deploying AI systems. Successful TDS submissions are technically detailed, timely, and specific. Authors can also benefit from editorial support, promotion, and potential payment opportunities, while building their reputation within the AI community.
This article is a year-end recap from Towards Data Science (TDS) highlighting the most popular articles published in 2025. The year was heavily focused on AI Agents and their development, with significant interest in related frameworks like MCP and contextual engineering. Beyond agents, Python remained a crucial skill for data professionals, and there was a strong emphasis on career development within the field. The recap also touches on the evolution of RAG (Retrieval-Augmented Generation) into more sophisticated context-aware systems and the importance of optimizing LLM (Large Language Model) costs. TDS also celebrated its growth as an independent publication and its Author Payment
This article by Kory Becker, a software developer, shares insights about prompt engineering, a crucial skill in working with large language models, gained from his AI certification.
An introduction to Network Analysis in Python using Pokemon data from the PokeApi. Written by Jacob Ingle, a writer for Towards Data Science.
Discussion on the efficiency of Random Forest algorithms for PCA and Feature Importance. By Christopher Karg for Towards Data Science.
Leverage LLM-enhanced natural language processing and traditional machine learning techniques are used to extract structure and to build a knowledge graph from unstructured corpus.
An End to End Example Of Seeing How Well An LLM Model Can Answer Amazon SageMaker-Related Questions
