Issue #319
May 20, 2026
Announcements
Ever wonder how we can turn thousands of unstructured news articles into structured, actionable insights?
In the latest post from Data4Sci, we dive into the fascinating process of transforming raw text from news articles into interconnected networks of information. If you're interested in Natural Language Processing (NLP), entity extraction, and how to connect the dots hidden across massive amounts of unstructured data, this is a must-read!
👉 From News Articles to Knowledge Graphs with spaCy and NetworkX
Check it out and Subscribe so you don't miss another post.
"LLMs in Production: From Language Models to Successful Products" by C. Brousseau and M. Sharp is for data scientists and machine learning engineers who have moved past the “cool demo” phase and now need to ship something people can use. The book focuses on the real work behind LLM products: choosing models, preparing data, building RAG systems, evaluating outputs, controlling cost, managing latency, and deploying reliably.
Its biggest strength is that it treats LLMs as production software, not magic. The authors connect familiar ML concerns—measurement, data quality, feedback loops, monitoring, and trade-offs—to newer LLM-specific patterns such as prompt design, fine-tuning, LoRA, RLHF, hosted APIs, Kubernetes deployment, and edge inference. The hands-on projects help ground the material, especially for readers who want more than another conceptual overview.
The book is not perfect. Some sections move quickly, and experienced MLOps engineers may wish for more depth on architecture, observability, or failure analysis. Its tooling choices may also date quickly, as LLM infrastructure continues to shift. Still, the core value holds: this is a practical guide to thinking like an engineer when working with language models. For anyone trying to turn LLM experiments into durable products, it is an easy book to justify buying.
- 1. There Is No ‘Hard Problem Of Consciousness' [noemamag.com]
- 2. Local AI Needs to be the Norm [unix.foo]
- 3. 26M function call model that runs on incredibly small devices [github.com]
- 4. Three new ways to build with real-world imagery and AI [mapsplatform.google.com]
- 5. Every AI Subscription Is a Ticking Time Bomb for Enterprise [thestateofbrand.com]
- 6. Is AI putting graduates out of work already? [economist.com]
- 7. Crypto Prediction Markets Explained [chainalysis.com]
- • $δ$-mem: Efficient Online Memory for Large Language Models (J. Lei, D. Zhang, J. Li, W. Wang, K. Fan, X. Liu, Q. Liu, X. Ma, B. Chen, S. Poria)
- • Drug Development Failure: How GLP-1 Development Was Abandoned in 1990 (J. S. Flier)
- • LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels (L. Maes, Q. L. Lidec, D. Scieur, Y. LeCun, R. Balestriero)
- • World models, artificial general intelligence and the hard problems of life–mind continuity: toward a unified understanding of natural and artificial intelligence (A. Safron, M. Levin, V. Klimaj, Z. Sheikhbahaee, D. Sakthivadivel, A. Razi, D. Ha, N. Hay, K. Schmidt, I. Rish, D. Krakauer, M. Mitchell, S. J. Gershman, J. B. Tenenbaum)
- • Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction (Z. Li, H. Zhang, C. Wei, P. Lu, P. Nie, Y. Lu, Y. Bai, S. Feng, H. Zhu, M. Zhong, Y. Zhang, J. Xie, Y. Choi, J. Zou, J. Han, W. Chen, J. Lin, D. Jiang, Y. Zhang)
- • Compared to What? Baselines and Metrics for Counterfactual Prompting (Z. Yang, M. Levy, Y. Goldberg, B. C. Wallace)
- • Superintelligent Retrieval Agent: The Next Frontier of Information Retrieval (Z. Yang, Q. Ma, J. Chen, A. Shrivastava)
- • Ten simple rules for optimal and careful use of generative AI in science (M. Helmy, L. Jin, A. Alhossary, T. Mansour, D. Pellagrina, K. Selvarajoo)
From Supervised FT to RLHF, LoRA, and Multimodal
All our videos are also available in our YouTube playlist.
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