LLM Apps, Crucial Data Skills, Multi-AI Agent Systems, and Other July Must-Reads

LLM Apps, Crucial Data Skills, Multi-Agent AI Systems, and Other July Must-Reads

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

If it’s already summer where you live, we hope you’re making the most of the warm weather and (hopefully? maybe?) more relaxed daily rhythms. Learning never stops, of course—at least not for data scientists—so if your idea of a good time includes diving into new 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 biggest splash among our readers last month, cover a wide range of practical topics—and many of them are geared towards helping you raise your own bar and expand your skill set. Let’s dive in!

Monthly Highlights

• What 10 Years at Uber, Meta and Startups Taught Me About Data Analytics
  Giving advice is easy; offering actionable, time-tested insights based on 10 years of diverse experience in data leadership takes quite a bit more effort —and in the case of Torsten Walbaum’s article, it very much pays off.
• How I Use ChatGPT as a Data Scientist
  Are we finally at the point where LLM-based tools can significantly streamline data professionals’ core tasks? As Egor Howell explains, if you make smart choices about how and where to integrate ChatGPT into your workflow, your productivity might already stand to reap major benefits.
• 330 Weeks of Data Visualizations: My Journey and Key Takeaways
  After creating weekly data visualization for more than five years, Yu Dong reflects on the value of consistency, and shares helpful pointers for current and aspiring data scientists who’d like to level-up their craft when creating charts, plots, and infographics.

• Building 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 no established best practices, and often, pioneers are left with no clear roadmap, needing to reinvent the wheel or getting stuck.” Fortunately, that’s no longer 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 started to discover all the various pain points and bottlenecks they create. Mariya Mansurova’s recent guide introduces one of the most promising strategies for addressing these challenges: multi-agent AI systems, where teams of agents, each with their own specialized “skill,” can collaborate with each other.
• 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 do not prepare students with the practical know-how needed in companies.” Sara aims to fill in this gap with recommendations for five areas data scientists should focus on in order to thrive in today’s job market.
• 17 (Advanced) RAG Techniques to Turn Your LLM App Prototype into a Production-Ready Solution
  For a one-stop, comprehensive resource you can refer to whenever you need to tweak, refine, or upgrade your retrieval-augmented generation system, make sure to bookmark Dominik Polzer’s recent contribution, which goes well beyond the basics to cover metadata, query routing, sentence-window retrieval, and much more.
• Fine-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 process of fine-tuning a smaller transformer model for an NLP task, working with a pre-trained encoder model with binary classes to identify clickbait vs. factual articles.

Our latest cohort of new authors

Every month, we’re thrilled to see a fresh group of authors join TDS, each sharing their own unique voice, knowledge, and experience with our community. If you’re looking for new 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.

Thank you for supporting the work of our authors! 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, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

LLM Apps, Crucial Data Skills, Multi-AI Agent Systems, and Other July Must-Reads was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.