Most companies have been exploring how they can leverage AI to enhance customer experience and streamline their business operations. The trend will continue through 2020 and beyond, but deploying in-house AI-based systems will remain an expensive affair, leading to the rise of AI-as service platforms where businesses can pay for algorithms to providers and use their own data.

Currently, Amazon Web Services (AWS), Microsoft Azure, Google Cloud, IBM Cloud and the likes of them provide Artificial Intelligence as a service. In future, we can anticipate wider adoption and a growing number of providers that are likely to offer tailored applications and services for specific tasks and at a lower cost, making it easier for companies to take advantage of the many benefits of it.

A variety of machine learning and Artificial Intelligence styles are available from various AI provider platforms. Since businesses must assess features and cost to determine what works for them, these differences can be more or less appropriate to an organization’s AI requirements. The specialised hardware required for some AI operations, such as GPU-based processing for heavy workloads, can be provided by cloud  service providers. For on-premise cloud to begin, expensive hardware and software must be purchased. AIaaS is prohibitively expensive for many firms when manpower, maintenance, and hardware upgrades are added in.

Organizations can learn what might be feasible with their data by using AI cloud services from Google Cloud Machine Learning, Microsoft Cognitive Services, and Amazon Machine Learning. Before committing, organisations can learn what works and enables scaling by having the chance to test the algorithms and services of other providers. The resources of these huge providers are available to support the scaling of compute capacity when anything is discovered that scales to requirements.

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