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In the first part, I described the context of how academic and industrial problem solving differ. The primary differences are the timeline of the solution…

Machine Learning in industrial settings operates in a different context than in academia. To increase success, one must deviate from the original Waterfall-like method…

One of the key and most overlooked aspect of Machine Learning is data labelling. I wrote about this here before, most recently in “Data…

“through Separation of Concerns” One of the most vibrant topics on the MLOps.community slack channel is the discussion around the difference between MLOps and DevOps. One…

And one factor that is suspiciously missing… Due to the cross-functional nature of Data Science projects, they are subject to many sources of risks.…

Or rather: How did we operate our company? Reflecting on the article: “Why Scrum is awful for data science”. Rather than going through the article…

According to the Gartner survey: “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organisation.”…

Decomposition is an essential system-design and problem-solving principle. Because of its importance, it pops up in many fields; it has many names: Separation of…

Despite all the hype and broken promises, Machine Learning has an incredibly strong advantage: It is a declarative paradigm. I will explain what this…

“What can AlphaGo teach us about how to run a business?” In a modern data-driven enterprise, there are three functions a data scientist can…