Rise of the Data Cloud- The Vs of Big Data were recently upgraded to 7 from 5, with Variability and Visualization joining the original five – Volume, Velocity, Variety, Veracity, and Value. This increase is a tacit acknowledgment that data is not just getting more expansive but also more complicated, if not convoluted.
The five key trends of data for 2021 will be AI, cloud containers, data democracy, as well as edge and serverless computing. Nothing exists in a vacuum and all of these trends were heavily affected by the 2020 pandemic, and, in many ways, these technologies all move in concert together, AI utilizes containers, which work well on serverless, which help democratize data.
Once the pandemic hit and companies all over the world were forced to provide work-from-home capabilities, these trends proved instrumental in keeping business as usual going – as far as business-as-usual could continue in a raging pandemic. All of these trends will continue to flourish in the coming years. They are not flash-in-the-pan momentary successes. They are sophisticated business-altering technologies that all executives should be aware of as well as continue to follow.
In 2021, the cloud will help AI further realize its abundant potential, perhaps not reach the heady heights of hype so many have promised but the massive amounts of data flowing into and through the cloud will definitely help turn promise into reality. AI is a difficult technology to implement, but the cloud and software like containers, Kubernetes, serverless computing, and powerful ML frameworks will help users create more responsible and scalable AI.
Over the past few decades, many key cloud-enabled breakthroughs helped raise AI from a technology that was floundering to one of almost limitless potential. These include the emergence of affordable parallel processing, Big Data, and its 7 Vs, as well as the access to improved ML algorithms from companies like Google, Microsoft, and Facebook. Because of their “build once, deploy anytime, anywhere capabilities”, cloud containers help facilitate the development and deployment of AI apps, which, in turn, democratizes AI.
Containers are an executable unit of software that consists of packaged application code along with all necessary software libraries and dependencies that run it. Containers are self-contained units that include everything needed for them to run and they can run anywhere, whether that is on the desktop, within traditional IT, or in the cloud.
Gartner believes containers are the preferred way to package machine learning models, which can be utilized from other external applications without any coding requirements. Containers can include the entire machine learning pipeline. They can scale as needed and be spun up in minutes. During ML training phases, containers can utilize multiple host servers, and then the trained models can be spread across multiple container endpoints and deployed wherever needed.
Although similar to a virtual machine (VM), containers don’t virtualize the underlying hardware, just the operating system, as well as the needed libraries and dependencies. This helps keep containers lightweight, fast, and highly portable. Containers also support modern development and architecture like DevOps, serverless computing, and microservices.