Google Cloud
BigQuery Explainable AI Now In GA To Help You Interpret Your Machine Learning Models
Explainable AI (XAI) helps you understand and interpret how your machine learning models make decisions. We’re excited to announce that BigQuery Explainable AI is now generally available (GA). BigQuery is the data warehouse that supports explainable AI in a most comprehensive way w.r.t both XAI methodology and model types. It does this at BigQuery scale, enabling millions of …
Use Graphs For Smarter AI With Neo4j And Google Cloud Vertex AI
In this blog post, we’re going to show you how to use two technologies together: Google Cloud Vertex AI, an ML development platform, and Neo4j, a graph database. Together these technologies can be used to build and deploy graph-based machine learning models. The code underlying this blog post is available in a notebook here. Why should you use …
Better Data For Better AI: New Speech Datasets And Benchmarks For Data
AI researchers and engineers need better data to enable better AI solutions. The quality of an AI solution is determined by both the learning algorithm (such as a deep-neural network model) and the datasets used to train and evaluate that algorithm. Historically, AI research has focused much more on algorithms than datasets, despite their vital …
Vertex AI NAS: Higher Accuracy And Lower Latency For Complex ML Models
Vertex AI launched with the premise “one AI platform, every ML tool you need.” Let’s talk about how Vertex AI streamlines modeling universally for a broad range of use cases. The overall purpose of Vertex AI is to simplify modeling so that enterprises can fast track their innovation, accelerate time to market, and ultimately increase return on …
How Data And AI Can Help Media Companies Better Personalize; And What To Watch Out For
Media companies now have access to an ever-expanding pool of data from the digitally connected consumer. And over the past two years, as content consumption and audience behaviors have shifted in response to the world around us, direct-to-consumer has only accelerated. As media organizations pivot from third-party to first-party data, this presents challenges with the …
Vertex AI NAS: Higher Accuracy And Lower Latency For Complex ML Models
Vertex AI launched with the premise “one AI platform, every ML tool you need.” Let’s talk about how Vertex AI streamlines modeling universally for a broad range of use cases. The overall purpose of Vertex AI is to simplify modeling so that enterprises can fast track their innovation, accelerate time to market, and ultimately increase return on …
Cloud Storage As A File System In AI Training
Cloud Storage is a common choice for Vertex AI and AI Platform users to store their training data, models, checkpoints and logs. Now, with Cloud Storage FUSE, training jobs on both platforms can access their data on Cloud Storage as files in the local file system. This post introduces the Cloud Storage FUSE for Vertex AI Custom Training. On AI Platform Training, the feature is …
Cloud Storage As A File System In AI Training
Cloud Storage is a common choice for Vertex AI and AI Platform users to store their training data, models, checkpoints and logs. Now, with Cloud Storage FUSE, training jobs on both platforms can access their data on Cloud Storage as files in the local file system. This post introduces the Cloud Storage FUSE for Vertex AI Custom Training. On AI Platform Training, the feature is …
Sowing The Seeds Of Ethical AI: 4 Tasks To Stay On Track
The future of AI is better AI—designed with ethics and responsibility built in from the start. This means putting the brakes on AI-driven transformation until you have a well-functioning strategy and process in place to ensure your models deliver fair outcomes. Failing to recognize this imperative is a threat to your bottom line. The following …
Scale Your data Science Workflows With The Vertex AI Workbench Notebook Executor
When solving a new ML problem, it’s common to start by experimenting with a subset of your data in a notebook environment. But if you want to execute a long-running job, add accelerators, or run multiple training trials with different input parameters, you’ll likely find yourself copying code over to a Python file to do …