TDS Editors

TDS Editors · Follow Published in Towards Data Science · Sent as a Newsletter · 6 min read · 12 hours ago — Feeling inspired to write your first TDS post before the end of 2024? We’re always open to contributions from new authors. And just like that, 2024 is (almost) in the books. It was a year of exciting transitions — both for the TDS team and, in many meaningful ways, for the data

TDS Editors · Follow Published in Towards Data Science · Sent as a Newsletter · 3 min read · 13 hours ago — Feeling inspired to write your first TDS post? We’re always open to contributions from new authors. It’s become more or less conventional wisdom that most machine learning projects don’t make it into production, and of those that do, many fail to deliver on their promise. We should always take sweeping claims like

TDS Editors · Follow Published in Towards Data Science · Sent as a Newsletter · 3 min read · 13 hours ago — Feeling inspired to write your first TDS post? We’re always open to contributions from new authors. When we encounter a new question, topic, or challenge, taking the first step forward is often the most difficult part. That’s the moment where self-doubt kicks in, our existing knowledge feels hazy and inadequate, and procrastination

TDS Editors · Follow Published in Towards Data Science · Sent as a Newsletter · 4 min read · 13 hours ago — Feeling inspired to write your first TDS post? We’re always open to contributions from new authors. Data science and machine learning professionals are facing uncertainty from multiple directions: the global economy, AI-powered tools and their effects on job security, and an ever-shifting tech stack, to name a few. Is it even possible

TDS Editors · Follow Published in Towards Data Science · Sent as a Newsletter · 4 min read · 13 hours ago — Feeling inspired to write your first TDS post? We’re always open to contributions from new authors. Before we get into this week’s selection of stellar articles, we’d like to take a moment to thank all our readers, authors, and members of our broader community for helping us reach a major milestone, as

TDS Editors · Follow Published in Towards Data Science · Sent as a Newsletter · 4 min read · 13 hours ago — Feeling inspired to write your first TDS post? We’re always open to contributions from new authors. When prompt engineering first emerged as a mainstream workflow for data and machine learning professionals, it seemed to generate two common (and somewhat opposing) views. In the wake of ChatGPT’s splashy arrival, some commentators declared it

TDS Editors · Follow Published in Towards Data Science · Sent as a Newsletter · 4 min read · 13 hours ago — Feeling inspired to write your first TDS post? We’re always open to contributions from new authors. When we think about problem-solving, our focus tends to be on the solving part: the powerful hack, a new magical tool, a few lines of code that make everything click into place. In reality, a lot

TDS Editors · Follow Published in Towards Data Science · 3 min read · 13 hours ago — Using synthetic data isn’t exactly a new practice: it’s been a productive approach for several years now, providing practitioners with the data they need for their projects in situations where real-world datasets prove inaccessible, unavailable, or limited from a copyright or approved-use perspective. The recent rise of LLMs and AI-generated tools has transformed the synthetic-data scene, however,

TDS Editors · Follow Published in Towards Data Science · 4 min read · 13 hours ago — As large language model-based workflows become both more sophisticated and more widespread, we’re seeing a growing number of novel approaches that help practitioners tailor (and improve) the models’ performance to specific projects and use cases. Many of our best-read articles in the past month zoomed in on this trend, with excellent guides for both novices and experiences