If you struggle with explaining why your data science project is difficult, it’s often down to data not being “ready to go”. But how to explain that to people that never worked with data themselves?
A great idea to help explain “what’s taking so long” to get results out of data is the concept of data readiness. It allows you to state exactly how far the data is away from where it needs to be to perform an analysis.
Data readiness consists of three bands, each of which we can divide further into more specific data readiness levels. …
Transformer models are all the rage these days. They have beaten the previously dominant long short-term memory networks in many state of the art NLP tasks. But what are they really about?
The key ingredient: Parallelization. As opposed to long short-term memory networks, Transformer models are not required to ingest sentences one word after another. Instead, they can be fed complete sentences at once.
Mikio Braun held a great talk at AMLD 2020 on the topic of getting ML into production. Mikio is staff scientist at GetYourGuide and was previously working as senior datascientist for Zalando. He might have an idea or two about applied ML, so I thought it’s worth sharing my takeaways from his talk.
His main theme: How do we “do ML”? Is algorithms + tools + data all there is to it? Maybe, but surely they all have further facets to explore. Mikio focuses on three topics:
Today, the fourth edition of Applied Machine Learning Days kicked off its keynotes. Here are my top three takeaways.
Data keeps being the main asset, but at the same time making this data F.A.I.R (findable, accessible, interoperable and reusable) keeps being a main challenge. Companies and their C-suit seem to realize this more and more.