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Artificial intelligence is used in many business scenarios. In some cases, organizations use data and machine learning to solve some of the most difficult problems and make incredible profits. According to the McKinsey Global Institute, companies that absorb AI in their workflows by 2025 are set to dominate the global economy in 2030.

McKinsey forecasts huge growth, predicting 120% more than cash flow growth. This is a pretty important number that Google Cloud wants to leverage. Recently, cloud service providers have released some AI / machine learning-oriented features to help organizations do just that. However, there are some challenges to doing this. According to Craig Wiley, director of product management for the Google Cloud AI platform, machine learning (ML) systems have the special ability to generate technical debt if not properly managed. He added to his blog post:

In addition to all the maintenance issues of traditional code, there is an additional set of ML-specific issues. ML systems have their own hardware and software dependencies that require data and code testing and validation. As the world changes, the deployed ML model deteriorates over time.

Google Cloud has several goals in its AL / ML product line designed to facilitate the execution of specific business tasks. The first product that integrates the development and operation of ML systems. That’s why the company announced earlier this year a hosted service for building and managing ML pipelines on its AI platform. Google Cloud announced in this month’s preview that it will soon expand its fully managed services in the ML pipeline.

The new managed service allows customers to build ML pipelines with TensorFlow Extended (TFX), pre-built components, and templates that reduce the effort required to deploy models.

The platform aims to simplify the management of large-scale models and allow data scientists to focus on models at risk of failing to meet their business goals. Google Cloud said in a statement that continuous monitoring will be possible by the end of 2020. The foundation of your organization’s new tools will serve as a machine learning metadata management service within the AI ​​platform. There should also be some impact, such as shorter development cycles, faster deployment speeds, and higher overall productivity.

Craig Wiley

Google Cloud has also introduced Google Workspace, which boasts many useful features. A brand new user experience that integrates tools such as chat, email, voice and video calling into one integrated interface. This is Google’s 14th rebranding of Business Productivity Suite, more recently known as GSuite. This suite includes Gmail, calendars, drives, documents, spreadsheets, slides, meetings, chat and more.

Google Meet has even made a recent makeover just in time for the school year. Jamboard is using a new digital whiteboard. This feature helps students and teachers collaborate while educators control who can edit the whiteboard. There is also a meeting room, attendance reports, Q & A features, and voting.

Google Cloud has a clear mission to enhance its capabilities in both education and enterprise areas. All of these have been given extensive nudge by the new coronavirus. The company told employees that they could work from home until July 2021, but competitor Microsoft has permanently allowed employees to work from home.

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