The most popular dataset on Kaggle is  Credit Card Fraud Detection. It’s an easy to understand problem space and impacts just about everyone. Fraud detection is a practical application that many businesses care about.  There’s a also something intrinsically cool about stopping crime with AI.

Here’s an interesting article on how to implement a fraud detection system with TensorFlow, PySpark, and Cortex.

While it would be cool to just build an accurate model, it would be more useful to build a production application that can automatically scale to handle more data, update when new data becomes available, and serve real-time predictions. This usually requires a lot of DevOps work, but we can do it with minimal effort using Cortex, an open source machine learning infrastructure platform. Cortex converts declarative configuration into scalable machine learning pipelines. In this guide, we’ll see how to use Cortex to build and deploy a fraud detection API using Kaggle’s dataset.

Over the last few years, cloud computing has become core to many enterprise IT models, and a number of enterprise architects are trying to make cloud systems as effective and beneficial as possible.

What role does Open Source play in making enterprises more agile to market fluctuations and the ability to seize opportunities? Given the footprint of open source tooling in big data and AI, this is not strictly a concern for infrastructure folks and app developers.

Enterprise Cloud Platforms for business This article covers the characteristics of the open source cloud and the open source based cloud computing layers, to help enterprises make the right choice. An open source cloud is any cloud service or solution that is developed using open source technologies and software. […]

Artificial Intelligence (AI) is usually not associated with creativity. Typically,  algorithms are used to automatize repetitive tasks or predict new outcomes based on previously seen examples. However, the rise of GANs (Generative Adversarial Networks) gives AI a touch of creative spark. Could this innovation automate the creative process?

Here’s an interesting article on the topic from Datanami.com

Let’s take a classic creative marketing example: product naming. The moment a product is pushed out onto the market, the most creative minds of the company come together to generate a number of proposals for product names that must sound familiar to the customers and yet are new and fresh too. Of all those candidates, ultimately only some will survive and be adopted as the new product names. Not an easy task!

If you’re a frequent reader of this blog or Data Driven listener, then you know that I am a fan of lifelong learning. This has helped me immensely in my career (and life in general).

This curated collection of online ML and Data Science courses comes courtesy of Delta Analytics, author and trainer Aurélien Geron, University of Wisconsin–Madison, AI researcher Goku Mohandas, University of Waterloo, National University of Singapore, and ETH Zurich.

If, after reading this list, you find yourself wanting more free quality, curated learning materials, check out the related posts at the bottom. Happy learning!

Nvidia CEO Jensen Huang holds up the Jetson Nano onstage during the GTC keynote address in San Jose, California — its smallest computer ever.

This new embedded computer in its Jetson line for developers deploying AI on the edge and the goal is to make them affordable.

The Jetson Nano developer kit is available today for $100, while the $129 Jetson Mini computer for embedded devices will be available in June.