The Delphina Blog

Catch up on the latest takes from the greatest minds in data science, as shared in the first seven episodes of the High Signal podcast from Delphina.

Explore recurring themes that hinder ML progress and get actionable strategies for fostering better collaboration and understanding between all parties involved.

Discover groundbreaking insights at the crossroads of AI, economics, and intelligent infrastructure with Michael I. Jordan in our inaugural High Signal podcast episode. Join us as we bring together leading voices in data science to help you advance your career and make a tangible impact in the world.

Discover the art of crafting high-ROI automated tests for fast-paced tech environments. Delphina engineer Thomas Barthelemy shares insights on effective testing strategies, taking a critical look at outdated models, and exploring new approaches for startups and beyond.

AutoML promised to revolutionize data science by automating the machine learning process, but it's fallen short. Unpack the limitations of AUtoML and why data science teams remain essential in tackling complex problems that extend beyond routine model optimization.

Data science teams perennially face a burning — yet often unspoken — question: what drives high value actions?

New PhDs can be total amateurs when it comes to the job market. Knowing these candidates will say some silly things — sometimes unintentionally — how can you separate the wheat from the chaff?

Unlike many functions, the returns to quality are highly non-linear in ML — and mediocre ML is often downright dangerous. Unpack why, how to identify mediocre ML, and what to do about it.

Behind the scenes, your team is increasingly worried Machine Learning is just a Mirage. Explore the SEVEN key personas on ML teams, and the unique challenges they each face in navigating the hype-vs-reality gulf of AI adoption.

If you aren’t involved in the day-to-day work of ML, you may assume data scientists and ML engineers spend their time fine-tuning transformer models and performing PhD-level math. Dive in to learn the truth.

For all the automation it promises, making machine learning happen is deeply manual work. Leaders need a realistic view of what it takes to build ML products that deliver value — and how to ensure their teams are actually doing that work.

Are you solving the right problems? When you don’t get the problem framing right, everything that comes next is a waste.

Today we dive into an uncomfortable question: ML ownership.

Deep diving into a question we get all the time from senior leaders: where does ML go wrong?

Data science and machine learning are transformational. They leave an impact like a crater: profound and enduring. But they're still way too hard.