Previously, only large companies with considerable resources could afford to experiment with Artificial Intelligence based technology. However, the scenario today, as changed with even small startups gaining access to cloud-based, prepackaged algorithms offering different AI models that can fast-track innovation
FREMONT, CA: Over the last decade, we have witnessed the rapid and growth and development of cloud space, driven by Amazon, Microsoft, Google, and IBM. One of the most significant trends emerging out of the industry is the drive towards serverless computing and the appliance model. Initially, cloud computing was used as a substitute by companies for colocation facilities and data centers. This was beneficial in terms of moving capital expenditure away from an equipment model to an operational model. Another benefit of this was arriving at a service model where cloud providers themselves took care of software updates as and when required. With advancements in cloud computing, more and more companies have made the jump to the cloud platform as a service model 9(PaaS) that delivers computing and software tools over the internet.
To support these changing trends, public companies have begun investing heavily in building or acquiring serverless components that have pre-built unit functionality. These tools allow organizations to test new concepts, iterate, and evaluate without taking on high risk or expense. Previously, only large companies with considerable resources could afford to experiment with Artificial Intelligence based technology. However, the scenario today, as changed with even small startups gaining access to cloud-based, prepackaged algorithms offering different AI models that can fast-track innovation.
Previously, innovations took up a chunk of time and money and were not always a viable option. With cloud computing in the picture, innovative ideas can be run through various simulations to see what kind of results to expect. Serverless public cloud infrastructure from all significant service providers come with readymade components like face-recognition tools and edge sensors that detect movement and weather conditions. Software developers can use these components to build a prototype that can be tested on a group of potential customers before putting the final product in the market. This makes it feasible for organizations to roll out the product in stages, over different locations, also providing an opportunity to correct any errors found in the product through feedback.