The fact that the tools used in the development of games are combined thanks to the game engines, provides a great deal of convenience to the developers. When these tools are together, there is no need to open a separate program for light and sound, and things go faster and in harmony with each other.
The same is true for artificial intelligence and machine learning. Google, one of the search engine giants, introduced innovations and services that will make the work of people working in this field faster and more efficient at the Cloud Next event held today. The company also announced its important collaborations at this event.
Vertex AI Workbench to be integrated into the Vertex AI service:
Talking about the Vertex AI service, which firstly offers the necessary tools for machine learning, Google announced the Vertex AI Workbench, which it will offer within this service. The innovation in question will act as an IDE (Integrated development environment) for machine learning and artificial intelligence.
This feature, which is often overlooked in platforms that offer cloud-based data processing, will prevent coders from jumping from program to program and will provide a more tidy working environment by combining their working environments.
BigQuery OMNI:
BigQuery Omni, which comes after Vertex AI Workbench, enables you to view, add, delete and change your data in different cloud environments from a single place, namely BigQuery. It should be noted that Amazon Web Services (AWS) or Microsoft Azure are mentioned here as different cloud environments.
In this context, although the query operations are performed on these platforms, you can see the results on the home page of Google Cloud.
Popular data processing service Apache Spark on Google Cloud:
The service, known as Apache Spark, which we can refer to as Spark for short, is a popular data engine that enables large-scale data operations to be performed. The service, which is very common in fields such as machine learning, artificial intelligence and data engineering, is integrated into Google Cloud serverless.
Previously, it was seen that cloud services offer serverless Spark service within their structure. But this meant extra effort, as the AI and code had to be tuned in that direction most of the time to specifically fulfill a request.
With the integration of the Serverless Spark service into the Cloud, people will upload their work to the Cloud to be performed, and the Cloud itself will do the rest. No adjustments or guidance will be required. The service in question; BigQuery will be integrated into Dataproc, Dataplex and Vertex AI.
Spanner and Google Earth
Continuing through the integration, Google offers the possibility to use the popular open-source database PostgreSQL interface in its global-scale database service Spanner. In the context of this integration, Postgres’ SQL dialect becomes workable on Spanner.
In the new service called Google Earth Engine, Google Earth data larger than 50 petabytes; BigQuery is made accessible to users of the Cloud’s machine learning technologies and Google Maps. Currently, Google Earth Engine is available as a preview version.
Google’s Tableau partnership (Looker partnership bears fruit)
Data analysis company Looker was part of Google Cloud in the past years. In this context, the platform, which offers a modeling language called LookML, is preparing to offer a more unified experience with Google Sheets. In this context, Connected Sheets technology, which enables data in Google Sheets to be matched with BigQuery, will also offer a unified experience with LookML.
In addition, Google announced its partnership with Tableau at the event, and with this partnership, Tableau users will have direct access to BigQuery and the service provided by Tableau will be accessed through Google Sheets.
As you can see, all the services introduced and the partnerships made are made to make the Google Cloud platform more useful and wide-ranging. Although we think that these developments and innovations will receive very good feedback, we will see over time what effect they will have in practice.
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