Keeping Up With Data — Week 14 Reading List
Who wouldn’t want to listen to new songs of Jimmy Hendrix, Kurt Cobain, Jim Morrison or Amy Winehouse? Thanks to the Lost Tapes of the 27 Club it is now possible. Well, actually the songs were not written by these amazing musicians who all died at the age of 27. AI algorithm generated a string of all-new hooks, rhythms, melodies, and lyrics, which were used by audio engineers to ‘compose’ the final songs.
Hope the following reading list will offer a decent lick to finish their new solos!
- Two steps towards a modern data platform: “Wouldn’t it be great if all data, from all departments, were easily accessible?”, asks the author. We have learnt that centralised data platform doesn’t come without complications. And so, the data mesh — as the new ideal — was born. But how can we transition from a central data platform to ‘data platform as a service’? No cookie cutter solution exists. Creating a lightweight central data platform and then scale and share it across teams is suggested as a way forward. (Gerben Oostra @ bigdatarepublic)
- Implementing a Data Lake or Data Warehouse Architecture for Business Intelligence? “BI is truly about using data of yesterday and today to make better decisions about tomorrow.” It should provide a single version of truth and provide descriptive and diagnostic analytics. What should be used? Data warehouse architecture providing stricter governance? Or data lake architecture with its flexibility? (Lan Chu @ Towards Data Science)
- Recent Advances in Language Model Fine-tuning: Fine-tuning a pre-trained language model has become the de facto standard for doing transfer learning in natural language processing. It helps these larg models to achieve the best performance or stay reasonably efficient. If you want to be served with an overview of the latest fine-tuning methods, spend 13 minutes reading Sebastian’s latest article. My take home. We can either try to follow the NLP space or just follow Sebastian. (Sebastian Ruder)
Let me end with yet another reading recommendation. Last night I read an article What Are the Values of Data, Data Science, or Data Scientists? on Harvard Data Science Review. ‘Value’ is a commonly used word in data science. But have you thought about the values of data scientists or value of data science education? Give it a try.