Vertex AI Training delivers a serverless approach to simplify the ML model training experience for customers. As such, training data does not persist on the compute clusters by design. In the past, customers had only Cloud Storage (GCS) or BigQuery (BQ) as storage options. Now, you can also use NFS shares, such as Filestore, for …
Developers and data scientists are increasingly turning to Google Kubernetes Engine (GKE) to run demanding workloads like machine learning, visualization/rendering and high-performance computing, leveraging GKE’s support for NVIDIA GPUs. In the current economic climate, customers are under pressure to do more with fewer resources, and cost savings are top of mind. To help, in July, …
Read more “Sharing is caring: How NVIDIA GPU sharing on GKE saves you money”
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. …
Read more “Scaling heterogeneous graph sampling for GNNs with Google Cloud Dataflow”
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. …
Read more “Use R to train and deploy machine learning models on Vertex AI”
Over the past few years, advances in training large language models (LLMs) have moved natural language processing (NLP) from a bleeding-edge technology that few companies could access, to a powerful component of many common applications. From chatbots to content moderation to categorization, a general rule for NLP is that the larger the model, the greater …
Read more “How Cohere is accelerating language model training with Google Cloud TPUs”
Today we are excited to announce the release of over twenty new BigQuery and BigQuery ML (BQML) operators for Vertex AI Pipelines, that help make it easier to operationalize BigQuery and BQML jobs in a Vertex AI Pipeline. The first five BigQuery and BQML pipeline components were released earlier this year. These twenty-one new, first-party, …
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 …
Alphabet CEO Sundar Pichai has compared the potential impact of artificial intelligence (AI) to the impact of electricity—so it may be no surprise that at Google Cloud, we expect to see increased AI and machine learning (ML) momentum across the spectrum of users and use cases. Some of the momentum is more foundational, such as …
One of the largest telecommunications companies in the world, Vodafone is at the forefront of building next-generation connectivity and a sustainable digital future. Creating this digital future requires going beyond what’s possible today and unlocking significant investment in new technology and change. For Vodafone, a key driver is the use of artificial intelligence (AI) and …
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Today, ML-driven innovation is fundamentally transforming computing, enabling entirely new classes of internet services. For example, recent state-of-the-art lage models such as PaLM and Chinchilla herald a coming paradigm shift where ML services will augment human creativity. All indications are that we are still in the early stages of what will be the next qualitative …
Read more “Cloud TPU v4 records fastest training times on five MLPerf 2.0 benchmarks”