Expectations for mobile apps have gone up steadily since the iPhone was released. But the choice of databases built for mobile apps has remained limited mostly to SQLite. RealmDB was created as a new option for mobile developers on iOS, Android,
Online advertising is a system of transactions that involve many different players. The user visits a publisher’s website; the publisher notifies an exchange that the user is on the website; the exchange presents an opportunity to a marketplace that ca...
The impact of artificial intelligence on our everyday lives will be so profound that our modern institutions will change completely. Employment, government, romance, social norms–all of these things will be upended. To see the signs of this coming,
If you wanted to build a machine learning model to understand human health, where would you get the data? A hospital database would be useful, but privacy laws make it difficult to disclose that patient data to the public.
Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process.
Data scientists need flexible interfaces for displaying and manipulating data sets. Data engineers need to be able to visualize how their data pipelines wire together databases and data processing frameworks.
Most tech companies are moving toward a highly distributed microservices architecture. In this architecture, services are decoupled from each other and communicate with a common service language, often JSON over HTTP.
Infrastructure is a term that can mean many different things: your physical computer, the data center of your Amazon EC2 cluster, the virtualization layer, the container layer–on and on. In today’s episode,
Deep learning uses neural networks to identify patterns. Neural networks allow us to sequence “layers” of computing, with each layer using learning algorithms such as unsupervised learning, supervised learning, and reinforcement learning.
Data science is typically done by engineers writing code in Python, R, or another scripting language. Lots of engineers know these languages, and their ecosystems have great library support. But these languages have some issues around deployment,