Building ML workflows in BigQuery the easy way, without code
A flexible, automated analytic workflow tool that integrates with BigQueryML allows no-code forecasting and model training.
A flexible, automated analytic workflow tool that integrates with BigQueryML allows no-code forecasting and model training.
Increased costs and wasted resources are on the rise as software systems have moved from monolithic applications to distributed, service-oriented architectures. As a result, over the past few years, interest in observability has seen a marked rise. Observability, borrowed from its control theory context, has found a real sweet spot for organizations looking to answer the question “why,” that monitoring alone is unable to answer.
Today’s governmental and educational organizations can’t fully use the wealth of data they possess to improve citizen and student outcomes. Government agencies often deal with disparate and siloed data that can impact real-time decision-making. Securely exchanging information and collaborating on data remains an essential task in almost every agency strategy.
When it comes to data, state of the art is an ever-moving target. There’s also a lot of hype around “cutting edge” that isn’t always grounded in reality. At Snowflake, we’re able to see how cutting-edge companies are actually working with data on our platform.
How BigQuery’s ML inference engine can be used to run inferences against unstructured data in BigQuery using Vertex AI pre-trained models.
Step-by-step instructions for building an image classifier with ResNet, Cloud Storage and BQML.