The much-anticipated Google Cloud Next 2021 has arrived, and just like last year, the event is virtual due to the global pandemic.
The event began today, October 12th, 2021, and will conclude on Thursday, October 14th, 2021. Google kicked off today’s presentation with a bang, announcing Intelligent Product Essentials and updates to Vertex AI, a managed Spark service, BigQuery, Contact Center AI (CCAI), and DocAI, among other data-focused technologies. These new analytics and industry solutions are intended to make it easier for businesses to extract value from data.
Below Is A Breakdown And Explanation Of Products Announced By Google:
1. Spark Services:
Spark is described as a serverless, and autoscaling implementation of Apache Spark that is currently accessible as a preview service. It has become a pervasive commodity environment for all types of analytics, data engineering, and machine learning workloads across the industry. Customers never had this provision or the code developed by Amazon Glue, unlike cloud providers who built serverless Spark services for themselves. Using Spark to execute a specific step in most data and AI pipelines, on the other hand, traditionally involves explicitly establishing a Spark cluster and dealing with the latency necessary for the cluster to set up.
“Spark has finally arrived in the cloud-native world,” stated Gerrit Kazmaier, Google’s VP and GM for Database, Data Analytics, and Looker. He added that “It enables data engineers and scientists to use Spark without having to worry about cluster end configurations.” We have made it a part of all of our data services. As a result, you may run it directly from BigQuery, Vertex AI, or Dataplex. It makes utilizing Spark so simple that our customers can continue to use their favourite frameworks and toolkits – they enjoy the data science experience, and they can now consume it in a cloud-native fashion.”
2. Intelligent Product Essentials:
These are intended to aid manufacturers in the development of hardware products. According to Google, they can use it to deploy AI-enabled products that can update over the air and provide insights using cloud analytics.
Intelligent Product Essentials can be utilized to provide a more tailored experience for customers. The service can also provide updates to products in the field, collect performance data, and enhance capabilities over time with revenue potential.
Intelligent Product Essentials also allows developers to create companion apps for smartphones, tablets, and PCs utilizing a prebuilt API that includes product and security, as well as device support.
3. Vertex AI Workbench:
At Google I/O 2021 in May, the company unveiled Vertex AI, a managed AI platform. Today, Vertex AI Workbench, a user experience for building and deploying AI models faster, is being added to the service, speeding up time-to-value for data scientists and their companies. The Workbench is a controlled notebook that may be used as an IDE (integrated development environment) for machine learning and AI projects. It connects Vertex AI’s fundamental components (such as its training and prediction services) with essential data platform components such as BigQuery, Dataproc, and Dataplex.
According to Google, “These features, delivered through managed notebooks, allow data scientists rapidly construct workflows and perform the coordination, transformations, security, and machine learning operations, all within Vertex AI,”. This type of integration has been generally lacking in cloud analytics settings, and bringing it all together saves data scientists, machine learning engineers, and data engineers from having to switch gears and losing their trains of thought as they leap from service to service.
Google has announced the wide availability of BigQuery Omni, which will allow businesses to examine data across Google Cloud, Amazon Web Services, and Microsoft Azure. The managed, cross-cloud analytics solution allows users to answer queries and communicate results across datasets from a single pane of glass, complementing Google’s Dataplex service (which will be broadly available this quarter).
5. Spanner and Looker:
Google is continuing to make Cloud Spanner, a fully managed relational database, available to customers via a PostgreSQL interface, (in preview), to complement the rest of its data-focused solutions. The interface supports several common PostgreSQL data types and SQL features, allowing schemas and queries written for the PostgreSQL interface to be migrated to another PostgreSQL environment. In addition, Google announced new Looker connectors that it claims would allow users to “operationalize analytics” and expand deployments more effectively. Looker’s semantic model will soon be available to Tableau customers and Connected Sheets users, with the Connect Sheets integration arriving in preview by the end of the year.
6. Google Earth Engine
Google Earth Engine is now available on Google Cloud, allowing users to analyze Google Earth Engine’s database of over 50 petabytes of satellite imagery and geographic datasets. Google Cloud customers will be able to integrate Earth Engine with BigQuery, Google Maps Platform, and Google Cloud’s AI technologies, giving data teams “a way to better understand how the world is changing and what actions they can take” — from lowering energy costs to identifying business risks and meeting customer needs, according to Google.
“Earth Engine has supported the work of researchers and nongovernmental organizations from around the world for over a decade, and this new integration brings the best of Google and Google Cloud together to empower enterprises to create a sustainable future for our planet and your business,” – Google.
7. CCAI and DocAI
CCAI Insights employs artificial intelligence to extract raw contact centre interaction data for usable information, whether the data came from a virtual or human agent. Smart Highlighters, which automatically emphasizes critical conversation moments such as when an agent authenticates or a customer confirms that their issue has been fixed, are included in the out-of-the-box analytics on customer conversations.
DocAI decodes contracts for crucial words such as start and end dates, renewal conditions, parties involved, and so on using NLP. It automatically recognizes significant terms and their relationships, perhaps resulting in contract processing that is faster and less expensive.
According to Google “All of these new features will help businesses adapt by making AI’s capabilities more accessible and focused on achieving business goals,”.
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