We've been overcomplicating machine learning for years. Sometimes we confuse it with the over-hyped artificial intelligence, talking about replacing humans with robotic reasoning when really ML is about augmenting human intelligence with advanced pattern recognition. Or we burrow into deep learning when more basic SQL queries would get the job done. But perhaps the greatest problem with ML today is how incredibly complicated we make the tooling because, as Confetti AI co-founder Mihail Eric has posited, the ML "tooling landscape with constantly shifting responsibilities and new lines in the sand is especially hardest for newcomers to the field," making it "a pretty rough time to be taking your first steps into MLOps." Normally we look to tooling to make tech easier.

Machine Learning or ML is fast becoming the buzzword of our time, but why are so many developers falling short when it comes to getting their heads round this essential skill? Here's why developers must tackle ML to get ahead and what's standing in their way. Let's face it, when it comes to AI, the future has very much arrived. This application of ML is everywhere right now, whether you're looking at self-driving cars or self-tuning database systems – it's impacting almost every industry on the market. Acquiring ML skills is a no-brainer for the ambitious developer, and the number of self-led courses and MOOCs doubled last year.