Posts in category

Programming


Developing autonomous driving technology is a battle with data, both from a volume and data format point of view. Sources include point cloud 3D data obtained from LIDAR, video data obtained from multiple cameras, GPS position information, millimeter-wave radar, steering and various sensor information. Even in a busy city, less than 1% of the raw data …

Increasingly more enterprises adopt Machine Learning (ML) capabilities to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization. Machine learning is inherently an experimental process requiring repeated iterations. An ML Platform standardizes the model development and deployment workflow to …

This blog presents an open-source solution to heterogeneous graph sub-sampling at scale using Google Cloud Dataflow (Dataflow). Dataflow is Google’s publicly available, fully managed environment for running large scale Apache Beam compute pipelines. Dataflow provides monitoring and observability out of the box and is routinely used to scale production systems to easily handle extreme datasets. …

R is one of the most widely used programming languages for statistical computing and machine learning. Many data scientists love it, especially for the rich world of packages from tidyverse, an opinionated collection of R packages for data science. Besides the tidyverse, there are over 18,000 open-source packages on CRAN, the package repository for R. RStudio, available as desktop …

When deploying models to the Vertex AI prediction service, each model is by default deployed to its own VM. To make hosting more cost effective, we’re excited to introduce model co-hosting in public preview, which allows you to host multiple models on the same VM, resulting in better utilization of memory and computational resources. The number of models …

Machine learning is a huge discipline, with applications ranging from natural language processing to solving partial differential equations. It is from this landscape that major frameworks such as PyTorch, TensorFlow, and Flux.jl arise and strive to be packages for “all of machine learning”. While some of these frameworks have the backing of large companies such as Facebook …

Moore’s Law needs a hug. The days of stuffing transistors on little silicon computer chips are numbered, and their life rafts — hardware accelerators — come with a price. When programming an accelerator — a process where applications offload certain tasks to system hardware especially to accelerate that task — you have to build a whole new software …

Machine learning is a huge discipline, with applications ranging from natural language processing to solving partial differential equations. It is from this landscape that major frameworks such as PyTorch, TensorFlow, and Flux.jl arise and strive to be packages for “all of machine learning”. While some of these frameworks have the backing of large companies such as Facebook …

  Researchers have pioneered a technique that can dramatically accelerate certain types of computer programs automatically, while ensuring program results remain accurate. Their system boosts the speeds of programs that run in the Unix shell, a ubiquitous programming environment created 50 years ago that is still widely used today. Their method parallelizes these programs, which …