And it's here to stay. Let's for a moment go down memory lane. Before "software ate the world", before software platforms took over? How would people access services? Either via word of mouth or a phone book, you would call and get someone to
René-Jean Corneille
I like to write about Product Engineering, Data and Artificial Intelligence through the lenses of my experience leading technical teams.
While microservices are motivated by domain driven design, which defines bounded business domains and ultimately manifests itself via loosely coupled services that can be independently updated, nanoservices are a response to the needs of machine learning engineering teams to manage an increasing number of models that act as a single
As someone that has been working in this field for about 12 years, it's very odd to see how the term machine learning has fallen out of fashion for the flashier AI. Though AI has always been quite popular, I do remember a distinctive period before the emergence
There is so much noise about the emergence of coding leveraging the latest AI advancements. I do think that a lot of it is very reactionary, some are quite sober takes. I've always been skeptical about hype because I'm a firm believer of the precautionary principle
Generative AI has taken the world by storm. It's everywhere. I'm currently flat hunting and you can actually search potential ads with natural language. As expected, new and promising technologies get this overinflated attention (though not always unwarranted) which leads to them being applied to anything
I love solving technical problems and never really considered the managerial track until well into my second role as a staff data scientist. I then realised that the more senior you become, most of your impact comes through others IC or managerial track. Which means that there's an
Latency is one of the main challenges to making machine learning impactful for an organisation. Depending on the latency requirements and the inference methods, the emphasis on latency can be either about cost efficiency and / or about scalability. Machine Learning Engineering teams need to be able to provide value and
The big data echosystem is still too big! There I said it. I still remember seeing this post years ago and as a young and upcoming data scientist and it resonated with me. When I tried to keep up to date with all the advancements in data infrastructure I felt