A Tour of End-to-End Machine Learning Platforms

Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

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The Quit Upwards Paradox

There can be many reasons why employees quit. Many people leave for jobs with better remuneration at other companies, yet the companies they leave behind hire people who do the same. Therein lies a paradox: why are fewer people promoted than hired from outside, especially since those who quit obtain pay rises significantly larger than those who stay at the same company?

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Lean Data and Machine Learning Operations

Lean Data and Machine Learning Operations (D/MLOps) is the adoption of the ‘lean’ philosophy from manufacturing. Its aim is to continuously improve the operation of data and machine learning pipelines.

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How to Get Started with Scala

Scala is a key language in the data space. While Python is the lingua franca of data science and machine learning, Scala frequently pops up in data engineering and backend systems. It provides a type-safe, functional layer on top of the battle-hardened JVM, which means it benefits from a rich ecosystem that’s available without all of Java’s boilerplate. Scala comes with its own REPL, so it is as easy and fast as Python to experiment with code. But what are the best ways to learn Scala? Here are a few of my suggestions.

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