Artificial intelligence is used in many business scenarios. In some cases, organizations are using data and machine learning to solve some of their toughest problems and have gotten incredible feedback. According to McKinsey World Institute, companies that will absorb AI into their workflows by 2025 are expected to dominate the global economy by 2030.
McKinsey even predicts colossal growth, forecasting a 120% increase in cash flow, a fairly large number that Google Cloud hopes to capitalize on. Recently, the cloud service provider released several AI / machine learning oriented capabilities to help organizations do just that. There are, however, some challenges to overcome, and depending on Craig Wiley, Director, Product Management, Google Cloud AI Platform, “Machine learning (ML) systems have a special ability to create technical debt if not well managed. He added in a blog post:
“They have all of the maintenance issues of traditional code, plus an additional set of ML-specific issues. ML systems have unique hardware and software dependencies, require testing and validation of data as well as code, and as the world changes, deployed ML models degrade over time ”
Google Cloud has a few goals with its line of AL / ML products designed to make it easier to perform certain business tasks. The first – to unify the development and operations of the ML system. As such, the company announced a hosted offering for building and managing ML pipelines on the AI platform earlier this year. Google Cloud has announced that it will soon be expanding a fully managed service for ML pipelines in preview this month.
“With the new managed service, customers can build ML pipelines using TensorFlow Extended (TFX), pre-built components, and templates that reduce the effort required to deploy templates.”
The platform aims to simplify the management of large-scale models and help data scientists focus on models that may not meet business goals. “Continuous monitoring is expected to be available by the end of 2020,” Google Cloud added in a statement. The foundation of the organization’s new tool serves as a service for managing machine learning metadata within the AI platform. It should also have several implications, including increasing productivity at all levels, reducing development cycles, increasing the speed of deployment, etc.
Google Cloud too introduced Google Workspace, which has a host of useful features. The all-new user experience brings together tools such as chat, email, voice and video calls in a single, unified interface. This is the 14th rebranding of Google’s enterprise productivity suite, more recently known as “G Suite”. The suite includes Gmail, Calendar, Drive, Docs, Sheets, Slides, Meet, Chat, and more.
Google Meet has even had a facelift, just in time for the school year. There is the use of new digital whiteboards with Jamboard. The feature helps students and teachers collaborate while educators maintain control over who can edit the whiteboard. There are also breakout rooms, attendance reports, question-and-answer capabilities, and polls.
Google Cloud is clearly on a mission to strengthen its functionalities in both education and business, all driven by the new Coronavirus. The company told its employees they could work from home until July 2021, while competitor Microsoft let employees work from home for good.