Adoption of the enterprise cloud has accelerated over the years, as more companies have used software-as-a-service models to process ever-larger sets of data.
Now, leading companies are learning that the cloud can do more than process data; It can drive business and revenue growth.
In particular, continuous advancements in artificial intelligence and machine learning are creating new opportunities for businesses to harness the power of their data, and the cloud provides the tools necessary to harness it.
Overcome early struggles
As early as 2015, many companies created large local data lakes in their initial attempt to realize the promise of “big data.” These centralized repositories of information, stored in a variety of formats, often became data graveyards, as many companies lacked the computing resources that early artificial intelligence technologies needed to obtain meaningful information. For example, graphics processing for images only was prohibitively expensive.
Back in those days, cloud AI platforms hadn’t yet matured enough to motivate companies to move data-intensive machine learning projects to a cloud environment. The well-documented potential of big data seemed frozen in time. Fortunately, this was only temporary.
Expanding what is possible
More recently, the advent of cloud-native data warehouses, such as snowflakes, knowledge graphs, and other technologies, has enabled companies to model data structures that are scalable in terms of storage and performance.
Major cloud computing vendors now offer product suites including model development, hosting, and machine learning operationalizations (MLOps), such as Amazon AWS SageMaker, released in 2017.
Additionally, cloud providers have also provided APIs for NLP (eg, Textract), prediction (Amazon Forecast) and artificial vision (Recognition) that are pre-trained and can be easily integrated into modern applications.
Follow the leader
Wipro FullStride Cloud Services Research shows that Cloud leaders will continue to expand their computing power for years to come, with a focus on 5G, Edge Computing, and Grid Computing technologies. Amid these investments, leaders are pairing key technologies with the cloud, most notably artificial intelligence. There are many reasons for this strategic decision.
The ever-expanding universe of artificial intelligence tools in the cloud has enabled product teams to dramatically reduce development costs and time-to-market, creating new possibilities for innovative companies.
The adoption of these technologies should not be random.
At Wipro, we have found that companies looking to migrate AI projects to cloud environments can adhere to several best practices to improve their chances of achieving optimal results.
Bringing AI to the cloud
Among other approaches, Wipro is based on E-IQ (business intelligence quotient), a framework that assigns an intelligence quotient to a given business process, revealing possible AI use cases in the context of five pillars: feel, decide, act, interact and adapt.
This benchmarking exercise can also help companies establish a roadmap for preparing projects for the cloud using an agile AI delivery model and reference architecture.
Once the use cases and supporting technical artifacts are identified, a bring-your-own-model approach can accelerate the migration of the model to optimal compute for endpoints in AWS SageMaker and other associated APIs.
Twice as beautiful
To ensure that models do not show “obsolescence” or “drift,” a robust MLOps framework guides onboarding and governance, enabling computational optimization and periodic recalibration of models during tagging when using AWS Ground Truth.
AI can be particularly useful in highly regulated industries such as financial services, which increasingly rely on complex models to inform their decision-making as regulators impose increasingly stringent validation requirements.
Using a smart approach to model validation and testing can ensure that internal model validation teams can effectively inventory their models, saving time and ensuring regulatory compliance in the process.
A glimpse into the future
These cloud investments are illuminating many powerful use cases for combining AI with the cloud. By leveraging a dynamic duo of artificial intelligence and cloud, companies are preparing to achieve a multitude of goals, including:
- New sources of income: A healthcare institution that moved the data associated with the ML models to the cloud was not only able to optimize costs, but also to monetize the model’s predictions. In this case, clients included research institutions that were able to bypass the data collection and aggregation processes required to build their own models and instead buy the results directly from the data source to speed up their research. The fees they paid covered the model development costs incurred by the health institution.
- Improved customer experiences: Cloud-based AI technologies can drive better customer experiences for all types of businesses, from taxi services to e-commerce stores. In the case of the former, a cabin display equipped with an AI-powered recommendation engine can show passengers personalized offers based on their destinations or movie recommendations built through knowledge charts in the cloud.
- Shape strategic results: With the help of artificial intelligence in the cloud, a CFO can infuse intelligence from internal and external data into the financial planning process to recommend initiatives to increase revenue. Similarly, a CMO can identify strategies to optimize marketing spend across a variety of product categories to maximize ROI.
For executives who rely solely on data sourced from enterprise resource planning / general accounting systems, this level of insight is simply not possible.
- Hyperautomation: Cloud AI platforms can enable intelligent automation to dramatically improve efficiencies related to any number of internal business processes. For example, a mobile app that makes API calls to Textract can extract information from documentation stored in the cloud to transform HR onboarding and can reduce the time it takes to complete administrative tasks from days to minutes.
Companies that are implementing AI in the cloud have already realized all the above results and many others.
As advances in cloud computing and AI / ML continue to unfold, the synergistic combination of these two technologies will continue to provide significant competitive advantages for innovative companies.
These competitive advantages will increasingly separate category leaders from the rest of the field.