machine learning models

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Artificial Intelligence and Machine Learning Bias has Dangerous Implications

Algorithms are everywhere in our world, and so is bias. From social media news feeds to streaming service recommendations to online shopping, computer algorithms—specifically, machine learning algorithms—have permeated our day-to-day world. As for bias, we need only examine the 2016 American election to understand how deeply—both implicitly and explicitly—it permeates our society as well. What’s often overlooked, however, is the intersection between these two: bias in computer algorithms themselves. Contrary to what many of us might think, technology is not objective...

To Trust Artificial Intelligence, It Must Be Open And Transparent. Period.

Machine learning has been around for a long time. But in late 2022, recent advancements in deep learning and large language models started to change the game and come into the public eye. And people started thinking, “We love Open Source software, so, let’s have Open Source AI, too.” But what is Open Source AI? And the answer is: we don’t know yet. Machine learning models are not software. Software is written by humans, like me. Machine learning models are trained; they learn on their own automatically, based on the input data provided by humans. When programmers want to fix a computer program, they know what they need: the source code. But if you want to fix a model, you need a lot more: software to train it, data to train it, a plan for training it, and so forth. It is much more complex. And reproducing it exactly ranges from difficult to nearly impossible.

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Why Data Scientists Love Kubernetes

Let's start with an uncontroversial point: Software developers and system operators love Kubernetes as a way to deploy and manage applications in Linux containers. Linux containers provide the foundation for reproducible builds and deployments, but Kubernetes and its ecosystem provide essential features that make containers great for running real applications...What you may not know is that Kubernetes also provides an unbeatable combination of features for working data scientists. The same features that streamline the software development workflow also support a data science workflow! To see why, let's first see what a data scientist's job looks like...

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