Home Technology Google is announcing new AI, intelligent analytics tools

Google is announcing new AI, intelligent analytics tools

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Google Cloud Next’s new AI tools focus on addressing common business challenges such as document structuring or inventory forecasts.

Google is launching a variety of t anAI and smaralytics tools at the Google Cloud Next Conference on Wednesday. The tools focus on AI applications for common business challenges such as document structuring or inventory forecasting.

First, Google announced the beta-based AI platform, a development platform that supports teams working on machine learning projects. It is designed for developers, data scientists and data engineers to use the same dashboard in the cloud console to share their workloads of model, training and scale.

Next, Google is launching new Cloud AutoML software that automates the development of machine learning models announced by Google last year. Google initially launched AutoML Vision, effectively extending Google’s Cloud Vision API to recognize entirely new custom image categories.

Google now has AutoML tables in beta, enabling you to build and deploy machine learning models in structured tabular datasets. Users can ingest BigQuery data and other GCP storage services into AutoML tables.

The new AutoML Video also enables developers to create a custom model that automatically classifies video content. Some clear case studies would be in the media and entertainment industry where companies could simplify tasks such as automated commercial removal or the creation of highlight rollers.

Google also implements AutoML Vision Edge to simplify training and deployment of precise, low latency custom ML models for edge devices. Google also announced Wednesday’s Document Understanding AI in beta. The serverless platform automatically classifies, extracts and enhances data from scanned or digital documents. It transforms unstructured document data into structured data and integrates stacks of Google’s Iron Mountain, Box, DocuSign, Egnyte, Taulia, UiPath and Accenture technology.

Google also announced that its AI Contact Center service is now beta. The new partners of the Contact Center AI include 8×8, Avaya, Salesforce and Accenture.

Google is introducing new ways to move data in Google Cloud and ways to clean, categorize and interpret data in the data analysis front. Cloud Data Fusion is a new, fully managed service that allows users to integrate and join data from different sources. It allows companies to take siloed data and prepare it for analysis of BigQuery.

Customers can now receive additional data in BigQuery with the expanded BigQuery data transfer service. BigQuery DTS automates data movement from SaaS applications to Google BigQuery on a scheduled, managed basis. It now supports over 100 popular SaaS apps, including Salesforce, Marketo, Workday and Stripe, in addition to Gogole’s own apps.

While Google facilitates data transmission to BigQuery, the exabyte serverless data warehouse is already growing rapidly. In the last year alone, Google said, the amount of data analyzed increased by more than 300 percent.

Meanwhile, data analysts will soon be able to build their own Cloud Dataflow SQL data pipelines. They can build data flow pipelines using SQL to automatically detect batch or stream data processing. Dataflow SQL uses the same BigQuery SQL dialect, allowing data analysts to use, for example, Dataflow SQL from the BigQuery UI.

Google introduces beta BigQuery BI Engine to analyze data. It allows users of BigQuery to interactively analyze complex data sets with high concentration and sub-second query response time. The tool is available through Google Data Studio. Third parties such as Looker and Tableau will also be able to leverage BI Engine in the coming months.

Given how often business users rely on tablets to analyze, Google also introduces connected sheets, a new type of tablet that works with a comprehensive BigQuery dataset.

Google is also expanding BigQuery ML, a tool that allows data analysts to build and deploy mass-data-set machine learning models with SQL. BigQuery ML now incorporates new models such as k-mean (beta) and matrix (alpha) clustering to produce customer segmentation and product recommendations.

Using BigQuery ML.Write, customers can also directly import and build TensorFlow Deep Neural Network (in alpha) models. Press the Quill It button on the right to paraphrase it.

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