Artificial intelligence (AI) and machine learning (ML) are significant technology trends that have been gaining popularity across the globe, in the past few years. Cloud-native ML/AI is helping organizations build AI/ML capabilities faster and save time spent in training models. Also, no prior knowledge is needed while working on ML/AI. It is expected that AI/ML will permeate smaller platforms, as the hardware required to run AI/ML continues to shrink in power requirements and size.

Some AI-driven processes remain too compute-intensive or security-sensitive to be distributed to smaller devices and gateways. However, the balance of enthusiasm and investment by leading cloud service providers is seen to be tipped toward the edge. This has led to an intense race for supporting AI/ML applications on smaller platforms. 

The big four companies in the cloud industry, Amazon, Microsoft, Google, and IBM, offer services that build and run various forms of AI in the public cloud. With the associated discipline of ML, AI holds the potential to make a logical sense of huge data volumes generated by connected technologies and transmit the instructions back to devices and gateways. This helps them act autonomously, thereby reducing the need for manual efforts.

The Rise of AI and ML

In the wake of the upsurge in the cloud market, leading providers, namely, Amazon, Microsoft, and Google, are doubling down on machine learning and artificial intelligence offerings. While the number of cloud-based services continues to spur, the prices are decreasing. Also, the number of affordable cloud facilities as well as compute engines for researchers and AI/ML developers have been increasing. Pre-trained models, which perform tasks like object detection, sentiment classification, and language translation are now readily available for the enterprise IT customers.

According to the International Data Corporation (IDC), the global spending on AI and cognitive systems will increase 3X by 2022, considering 2018 as the base year. This is significantly driven by investment of businesses in projects that leverage AI/cognitive capabilities. The market for AI is on an upward spiral, with cloud vendors eyeing to leverage machine learning, deep learning and AI and move quickly in this emergent marketplace.

Early adopters of ML and AI technologies across industry sectors have leveraged these technologies as part of their cloud transformation strategies. These strategies have further empowered organizations personalize their relationship with customers, keep their business running, and prevent fraudulent losses. 

The Pathway Moving Forward

Data analysis, creation and management, are major surging trends witnessed lately. Whether in the cloud or on-premise, channelization and composition of data remains a time-intensive task. This not only wastes time when data analysts work on basic activities, but also is non-beneficial to organizations who keep their data scientists focused on low lying activities. Also, the reusability and reproducibility of data will be cost-intensive for organizations who move their data to and from cloud environments.

In a bid to be on the lucrative side, it is important for organizations to build persistent platforms for preparation and pipelining of data such as blending data and imposing operational activities. As a result, it will simplify the processes and make them faster while preventing access to the unauthorized users.

Organizations must also focus on addressing challenges faced in the adoption of AI and ML, so as to use these technologies for drastic business revolutionization, process improvement, and productivity enhancement. The key is to mitigate challenges and leverage benefits of the core capabilities of cloud-native ML and AI. Organizations that adhere to right approaches to deploy AI-driven solutions are highly likely to remain at the forefront of the digital transformation curve in the upcoming years.

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