New features for Google Cloud’s unified data and AI platform | Google Cloud Blog

Without AI, you’re not getting the most out of your data.
Without data, you risk stale, out-of-date, suboptimal models.

But most companies are still struggling with how to keep these highly interdependent technologies in sync and operationalize AI to take meaningful action from data.

We’ve learned from Google’s years of experience in AI development how to make data-to-AI workflows as cohesive as possible and as a result our data cloud is the most complete and unified data and AI solution provider in the market. By bridging data and AI, data analysts can take advantage of user-friendly, accessible ML tools, and data scientists can get the most out of their organization’s data. All of this comes together with built-in MLOps to ensure all AI work — across teams — is ready for production use. 

In this blog we’ll show you how all of this works, including exciting announcements from the Data Cloud Summit:

  • Vertex AI Workbench is now GA bringing together Google Cloud’s data and ML systems into a single interface so that teams have a common toolset across data analytics, data science, and machine learning. With native integrations across BigQuery, Spark, Dataproc, and Dataplex data scientists can build, train and deploy ML models 5X faster than traditional notebooks. 

  • Introducing Vertex AI Model Registry, a central repository to manage and govern the lifecycle of your ML models. Designed to work with any type of model and deployment target, including BigQuery ML, Vertex AI Model Registry makes it easy to manage and deploy models. 

Use ML to get the most out of your data, no matter the format

Analyzing structured data in a data warehouse, like using SQL in BigQuery, is the bread and butter for many data analysts. Once you have data in a database, you can see trends, generate reports, and get a better sense of your business. Unfortunately, a lot of useful business data isn’t in the tidy tabular format of rows and columns. It’s often spread out over multiple locations and in different formats, frequently as so-called “unstructured data” — images, videos, audio transcripts, PDFs — can be cumbersome and difficult to work with. 

Here, AI can help. ML models can be used to transcribe audio and videos, analyze language, and extract text from images—that is, to translate elements of unstructured data into a form that can be stored and queried in a database like BigQuery. Google Cloud’s Document AI platform, for example, uses ML to understand documents like forms and contracts. Below, you can see how this platform is able to intelligently extract structured text data from an unstructured document like a resume. Once this data is extracted, it can be stored in a data warehouse like BigQuery.

Source Link

Read in Hindi >>