The Delphina Blog

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

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.

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.

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.