Feeling inspired to write down your first TDS post? We’re at all times open to contributions from latest authors.
If it’s already summer where you reside, we hope you’re taking advantage of the nice and cozy weather and (hopefully? perhaps?) more relaxed day by day rhythms. Learning never stops, in fact—not less than not for data scientists—so in case your idea of an excellent time includes diving into latest challenges and exploring cutting-edge tools and workflows, you’re in for a treat.
Our July highlights, made up of the articles that created the largest splash amongst our readers last month, cover a big selection of practical topics—and lots of of them are geared towards helping you raise your personal bar and expand your skill set. Let’s dive in!
Monthly Highlights
- Constructing LLM Apps: A Clear Step-By-Step Guide
Many ML practitioners have great ideas for AI-based products, yet, as Almog Baku points out, “there are not any established best practices, and infrequently, pioneers are left with no clear roadmap, needing to reinvent the wheel or getting stuck.” Fortunately, that’s not the case, now that Almog has put together a blueprint for navigating the complex landscape of LLM-native development. - Multi AI Agent Systems 101
Soon after LLMs went mainstream, product engineers began to find all the assorted pain points and bottlenecks they create. Mariya Mansurova’s recent guide introduces one of the promising strategies for addressing these challenges: multi-agent AI systems, where teams of agents, each with their very own specialized “skill,” can collaborate with one another. - The 5 Data Science Skills You Can’t Ignore in 2024
In her excellent career-focused roundup, Sara Nóbrega observes that “while universities and formal education provide some essential skills, they often don’t prepare students with the sensible know-how needed in firms.” Sara goals to fill on this gap with recommendations for five areas data scientists should give attention to with a view to thrive in today’s job market. - 17 (Advanced) RAG Techniques to Turn Your LLM App Prototype right into a Production-Ready Solution
For a one-stop, comprehensive resource you’ll be able to seek advice from every time you should tweak, refine, or upgrade your retrieval-augmented generation system, make sure that to bookmark Dominik Polzer’s recent contribution, which fits well beyond the fundamentals to cover metadata, query routing, sentence-window retrieval, and far more. - Superb-Tune Smaller Transformer Models: Text Classification
We round out our monthly lineup with a standout project walkthrough, courtesy of Ida Silfverskiöld: it patiently outlines the strategy of fine-tuning a smaller transformer model for an NLP task, working with a pre-trained encoder model with binary classes to discover clickbait vs. factual articles.
Our latest cohort of recent authors
Every month, we’re thrilled to see a fresh group of authors join TDS, each sharing their very own unique voice, knowledge, and experience with our community. In case you’re in search of latest writers to explore and follow, just browse the work of our latest additions, including Mengliu Zhao, Robbie Geoghegan, Alex Dremov, Torsten Walbaum, Jeremi Nuer, Jason Jia, Akchay Srivastava, Roman S, James Teo, Luis Fernando PÉREZ ARMAS, Ph.D., Lea Wu, W. Caden Hamrick, Jack Moore, Eddie Forson, Carsten Frommhold, Danila Morozovskii, Biman Chakraborty, Jean Meunier-Pion, Ken Kehoe, Robert Lohne, Pranav Jadhav, Cornellius Yudha Wijaya, Vito Rihaldijiran, Justin Laughlin, Yiğit Aşık, Teemu Sormunen, Lars Wiik, Rhea Goel, Ryan D’Cunha, Gonzalo Espinosa Duelo, Akila Somasundaram, Mel Richey, PhD, Loren Hinkson, Jonathan R. Williford, PhD, Daniel Low, Nicole Ren, Daniel Pollak, Stefan Todoran, Daniel Khoa Le, Avishek Biswas, Eyal Trabelsi, Ben Olney, Michael B Walker, Eleanor Hanna, and Magda Ntetsika.
