When developing a real-time architecture, there are two fundamental questions that you need to ask yourself in order to make the right technology choice: Freshness – how fast does the data need to be available? Query latency – how fast do you need to be…

Monitoring machine learning (ML) models in production is now as simple as using a function in BigQuery! Today Google introducing a new set of functions that enable model monitoring directly within BigQuery. Now, you can describe data throughout the model workflow by profiling training or…

Embeddings represent real-world objects, like entities, text, images, or videos as an array of numbers (a.k.a vectors) that machine learning models can easily process. Embeddings are the building blocks of many ML applications such as semantic search, recommendations, clustering, outlier detection, named entity extraction, and…

Google are excited that bidirectional data sharing between BigQuery and Salesforce Data Cloud is now generally available. This will make it easy for customers to enrich their data use cases by combining data across different platforms securely, without the additional cost of building or managing…