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Photo by Sajad Nori on Unsplash

In a previous article, I laid out a simple framework to navigate the various challenges involved with using data to build machine learning (ML) models. This framework is illustrated in the diagram below.


How strong are your data jujitsu skills?

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Photo by Josefina Di Battista on Unsplash

A few weeks ago I hosted a series of roundtable discussions with a group of engineering leaders from Atomico’s portfolio companies. The theme of these discussions was on data, specifically the challenges of data in an AI company. The topic is admittedly broad and open ended, but it is an important one nonetheless.

A significant portion of building AI, or to be more specific machine learning (ML) models, is centered around data. ML models are mostly concerned with getting data, cleaning it, transforming it, visualizing it and finally using it to build models. A recent survey by Anaconda revealed that…


It’s hard, complex, multidisciplinary yet exceptionally rewarding

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Photo by Hush Naidoo on Unsplash

My professional software experience has for the most part been in the B2B sector, working products spanning video-conferencing, distributed file systems and SQL engines. My most recent experience at Kheiron is my first foray into AI and healthcare. In a previous article, I covered the differences between AI products and software ones. This article is concerned with the differences I observed in developing software for the healthcare sector.

First a bit of context about the sector, mostly driven from my own impressions with empirical evidence when available.

The healthcare market is massive. In the US alone, this market is estimated…


Most AI products are primarily concerned with making inferencing decisions. An AI model receives some input which will run through the model resulting in some decision, oftentimes called a prediction or inference. Depending on the nature of the AI model, the precision of the decisions it makes can have profound effects on its usage and ultimate success.

Consider a recommendation engine, perhaps one that recommends what movies to watch based on your viewing history. The AI model in this case is making a predictive decision based on your own viewing history. We’re all fairly accustomed to these recommendation engines on…


A case study of C3.ai

I have been waiting for a pure-play artificial intelligence (AI) company to file for an IPO to get an in depth look at the company’s business model and how varied it is to traditional software products. My interest lies in trying to understand the impact of the long tail of AI on the company’s business model and financials. I had written about this topic earlier in a couple of previous posts. The first offered a glimpse on how building AI products is fundamentally different than traditional software. …


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Image source: Annie Sprat

Equity is a significant portion of a startup’s compensation package, in fact it is arguably one of the top reasons why people join startups. In spite of the popularity and importance of equity, I’ve seen that the way it is used can vary quite significantly between companies. While I do not posit that there is a right approach to equity compensation, I do believe that there are certain outcomes that should be universally applicable. These are outlined below.

Equity >> Cash

This one should be non-controversial. As an employee joining a startup, I want to participate in the potential economic growth of the…


If there is a single unifying factor that I have found across all startups, both ones I worked at or advised, it is the following: There will always be more work than the resources available to do it. Always.

There are two consequences to the above. The first, is being ruthless with prioritizing work. You have to pick your bets very carefully and where you choose to invest your scarce resources — a topic for another post. The second, is the importance of excelling in recruiting. …


On May 11th 1997 Deep Blue, a computer program, beat Gary Kasparov; chess grandmaster and arguably one of the greatest players of all time. That was the first time that a computer had defeated a chess grandmaster under tournament conditions. That moment represents a watershed moment between man and machine, one that offers a tremendous amount of opportunity and collaboration between both.

A month after the match, Kasparov wrote the following in his book, Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins,

“The scrum of photographers around the table doesn’t annoy a computer. There is no looking into…


How to build generalizable AI products

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Image source: Unsplash

In a previous article, I outlined my recent experiences with building AI products and their differences relative to traditional software. In the second part of this series, I will take this one step further and discuss how AI products (models) are dynamic entities that constantly need to be optimized along with the implications of this phenomena.

Let’s imagine for a moment that we are working on building a novel AI product. Our product will receive as input an image of a car crash and generate an output decision: whether the car is a total loss or not…


On the difference between ML and software products

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Image source: https://unsplash.com/photos/ndja2LJ4IcM

I recently joined a series-A startup — Kheiron Medical — that is a pure-play machine learning (ML) company. Kheiron develops AI-based products that are used by radiologists to detect breast cancer in women.

One of my motivations for making this move was to gain a better understanding and learn about ML companies and how they differ from traditional software development, which is my background. In this post, I will cover the differences between traditional software development and ML from a developers perspective. …

Karim Fanous

Tech leadership at various early stage startups: Qumulo, Dremio and now Kheiron Medical

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