The must-listen perspectives on data and AI

More insights from the greatest minds in data science, now on High Signal

The rise of data slop

With AI tools, even well-meaning employees can generate misleading analytics without realizing it. Here's how to spot — and stop — data slop.

The vibes about A/B testing are wrong

Why anti-A/B testing sentiment is running rampant — and when leaders need to rely on taste, not data.

The paradox of optimism in data science

Data science leaders must balance belief in the transformative power of ML & AI with reality: major data initiatives are risky and take months to move from conception to production.

The greatest minds in data science

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.

5 ways stakeholders stall out critical ML initiatives

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

Our new High Signal podcast

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.

Truth, lies, and ROI

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 Delphina engineer Thomas Barthelemy shares insights on effective testing strategies, taking a critical look at outdated models, and exploring new approaches for startups and beyond.

Why AutoML failed to live up to the hype

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.

What advanced analytics teams are doing that you aren’t

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

Why PhDs whiff the onsite and how to find a diamond in the rough

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?

The danger zone in data science

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.

The seven personas of machine learning

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.

The six most painstaking steps in machine learning

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.

The paradox of machine learning – what leaders need to know

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.

The costliest mistake in machine learning

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

Who should own machine learning?

Today we dive into an uncomfortable question: ML ownership.

The five breaking points for ML in the business

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

Why GenAI will transform data science & machine learning workflows

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