The 5 Principles That Turn ML Projects Into Business Impact

Why 70% of machine learning projects fail to deliver value, and the engineering-first approach that ensures yours won't be one of them

When I was at Meta, I got to learn about a simple word: IMPACT. This word was literally absolutely everywhere. You want to do something, and your manager will ask: But Rahul, what’s the Impact. You cannot do anything at Meta without getting this word thrown at you. Honestly, while I hated it back then, now it makes me think before focussing on any problem at hand.

As machine learning engineers, we aim to build sophisticated models, fine-tune complex architectures, and push the boundaries of algorithmic performance. Yet, a harsh reality persists: a staggering number of our projects never deliver tangible business value. After spending years of my life on a hundreds of ML deployments, I’ve noticed why up to 70% of projects falter not on the algorithm, but in the gap between the model and the real world.

And, the secret to success isn't in a more complex model or a novel architecture. It's found in a disciplined, engineering-first approach that prioritizes smart problem selection, continuous iteration, and a relentless focus on production realities.

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The Issues with Current ML

Alright, let's get to the gist of what I am saying. Honestly, I have noticed that the technical challenge of training a model is often the easiest part of the journey. The real hurdles are the not so fun, often-overlooked steps that come before and after. Here’s where most projects stop delivering Impact:

• Poor Problem Formulation: We start with "Let's build a recommender system" instead of "We need to increase user engagement by 15% on product pages, which translates to $X in new revenue." We chase a solution before we've even defined the problem.