Leadership & Management

When starting their AI initiatives, many companies are trapped in silos and treat AI as a purely technical enterprise, sidelining domain experts or involving them too late. They end up with generic AI applications that miss industry nuances, produce poor recommendations, and quickly become unpopular with users. By contrast, AI systems that deeply understand industry-specific … Read more...

When I talk to corporate customers, there is often this idea that AI, while powerful, won’t give any company a lasting competitive edge. After all, over the past two years, large-scale LLMs have become a commodity for everyone. I’ve been thinking a lot about how companies can shape a competitive advantage using AI, and a recent article in the Harvard Business Review (AI Won’t … Read more...

Various businesses use machine learning to manage and improve operations. While ML projects vary in scale and complexity requiring different data science teams, their general structure is the same. For example, a small data science team would have to collect, preprocess, and transform data, as well as train, validate, and (possibly) deploy a model to do a single … Read more...

Although scientists, engineers, and business mavens agree we might have finally entered the golden age of artificial intelligence when planning a machine learning project you have to be ready to face much more obstacles than you think.

Deep learning algorithms like AlphaGo are breaking one frontier after another, proving that machines can already be able to play complex … Read more...

As Machine Learning (ML) is becoming an important part of every industry, the demand for Machine Learning Engineers (MLE) has grown dramatically. MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the implementation challenges that come up — from building out training infrastructure to … Read more...

“I manage products, not people,” is a common quip from those supervising technical teams. It’s also dead wrong.

If you’re struggling to build great products and things are falling short, you may not have a product problem. You might have a people problem — 65% of failures of VC-backed startups happen because of people issues.

In this article, we’ll share the … Read more...

I was recently asked by a startup I’m consulting to give my opinion about the structure and flow of data science projects, which made me think about what makes them unique. Both managers and the different teams in a startup might find the differences between a data science project and a software development one unintuitive and confusing. If not stated and accounted for … Read more...