Julia and The Reincarnation of Lisp
Background I have programmed exclusively in C for five years so far. I can say from experience, after one and half years in C, I was beginning to understand pointers, mal...
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Quantum Computing + Bayesian Networks = Future AI
Burma Shave ads in the service of Data Science...
Web Personalisation with Drupal and Machine Learning
In this modern age, self-driving cars, voice-based assistants, social media feeds, and more are the tools fuelled by the technological marvel of the 21st century called m...
Leaving Money on the Table and the Economics of Composable, Reusable Analytic Modules
Money on the Table..MOTT. When I was the Vice President of Advertiser Analytics at Yahoo, this became a key focus guiding the analytics that we were delivering to adver...
Visually Explained: What Companies Get Wrong About Failure?
Many traditional businesses hate to fail. They embrace concepts such as Fail Fast in public, but deep inside their hearts, they are scared to fail. Failure is what gives ...
MLOps vs. DevOps: the similarities and differences
In this post, we explore two terms which are becoming relatively common in professional machine learning applications – MLOps and DevOps The term MLOps refers to a set ...
ModelOps: The Key to Unlocking the Future of AI
There are several narratives that permeate around AI today. It has great promise, but it is not without pitfalls. Many early adopters have invested millions, but remain u...
simdjson-go: Parsing gigabytes of JSON per second in Go
Written by Frank Wessels Introduction JSON has established itself as the “lingua franca” of the web. As such the parsing performance of JSON is hugely importa...
You should stop worrying about R vs. Python vs. whatever the tool
The big question and its answer are about solving problems. All the rest are only tools to get them done. In a world that is daily becoming more binomial (and boring beca...
25 Great Articles and Tutorials on Classification Methods
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlati...