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March is in the books, and in this week’s Variable we celebrate our most-read and -shared articles of the past month — read on to explore emerging trends in enterprise computing setups, catch up on novel approaches to LLM optimization, and get up-and-running with Python dependency management (among other topics).
Before we jump in, we also wanted to share our latest resource geared towards TDS authors: a quick(ish) guide to formatting your draft on our recently launched Contributor Portal. If you’re an existing or aspiring author, this one’s for you!
Forget About Cloud Computing. On-Premises Is All the Rage Again
The most widely circulating article we published in March didn’t tackle data or machine learning directly, but focused on a topic that touches the work of virtually all tech practitioners. Ari Joury drew our attention to the diminishing popularity of cloud computing, and outlined the reasons both startups and more established organizations are giving on-premise setups another look.
How to Switch from Data Analyst to Data Scientist
Career changes are never easy — especially in a competitive job market. Marina Wyss shares actionable advice for those who’d like to jump from data analytics to DS.
LLM + RAG: Creating an AI-Powered File Reader Assistant
Leverage AI to boost your productivity: follow along Gustavo Santos‘ hands-on guide to building a question-answering chatbot.
Other Top Reads
What else was on our readers’ radar last month? Python, fine-tuning, heatmaps, and more:
- In a clear and focused post, Vyacheslav Efimov introduced us to the fundamental principles of Python dependency management.
- How can you make your LLM more accurate? Read the latest beginner-friendly guide by Sarah Schürch, unpacking the role of RAG and fine-tuning.
- Learn how to visualize trends and outliers with non-linear color—Lee Vaughan‘s latest hands-on tutorial is all about well-designed heatmaps.
- Curious to learn about fine-tuning options beyond LoRA? Martin Görner‘s overview walks us through some of the most promising recent alternatives.
- From Tula Masterman, a new deep dive presents the “pyramid search approach” to leveraging agentic knowledge distillation.
Meet Our New Authors
Explore top-notch work from some of our recently added contributors:
- Qian (Alex) Wan and Eli Ruoyong Hong shared a fascinating look into the challenges of Japanese-Chinese translation with generative AI.
- Archit Datar‘s debut TDS article focuses on uncertainty quantification in machine learning and introduces a new Python package.
- Jordy Davelaar took a break from his astrophysics work to write on March Madness and the intersection of data science and sports analytics.
Contribute to TDS
We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
