Enterprise machine learning is becoming increasingly accessible as technology providers make it easier to integrate sophisticated AI models directly into mobile applications. By connecting cloud-based machine learning platforms with on-device processing capabilities, businesses can build applications that analyze information in real time while continuing to improve models through data collected in the field. This combination can provide faster performance, greater flexibility, and more powerful enterprise mobile experiences.
The integration of cloud AI with mobile development platforms also helps organizations overcome some of the complexity involved in deploying machine learning applications. Developers can train models using extensive cloud computing resources and then implement those models on mobile devices for real-time use. As businesses expand their use of artificial intelligence, these hybrid approaches can support applications ranging from natural language processing and predictive analytics to image recognition and automated decision-making while giving enterprises greater control over how and where data is processed.
IBM and Apple have agreed to link Watson’s machine learning development platform so that trained models in Watson can be executed on the iOS platform, but apparently only within the Mobile First initiative which restricts availability:
“Integrating Watson tech into iOS is a fairly straightforward workflow. Clients first build a machine learning model with Watson, which taps into an offsite data repository. The model is converted into Core ML, implemented in a custom app, then distributed through IBM’s MobileFirst platform.
Introduced at the Worldwide Developers Conference last year, Core ML is a platform tool that facilitates integration of trained neural network models built with third party tools into an iOS app. The framework is part of Apple’s push into machine learning, which began in earnest with iOS 11 and the A11 Bionic chip.
“Apple developers need a way to quickly and easily build these apps and leverage the cloud where it’s delivered,” said Mahmoud Naghshineh, IBM’s general manager, Apple partnership.
On that note, IBM is also introducing IBM Cloud Developer Console for Apple, a cloud-based service that simplify the process of building Watson models into an app. The arrangement allows for back-and-forth data sharing between an app and its backbone database, meaning the underlying machine learning model can improve itself over time if the client so chooses. Users can also tap into IBM cloud services covering authentication, data, analytics and more.
“That’s the beauty of this combination. As you run the application, it’s real time and you don’t need to be connected to Watson, but as you classify different parts [on the device], that data gets collected and when you’re connected to Watson on a lower [bandwidth] interaction basis, you can feed it back to train your machine learning model and make it even better,” Naghshineh told TechCrunch.
Apple and IBM first partnered on the MobileFirst enterprise initiative in 2014. Under terms of the agreement, IBM handles hardware leasing, device management, security, analytics, mobile integration and on-site repairs, while Apple aids in software development and customer support through AppleCare.
IBM added Watson technology to the service in 2016, granting customer access to in-house APIs like Natural Language Processing and Watson Conversation. Today’s machine learning capabilities are an extension of those efforts.”
The convergence of enterprise machine learning, cloud computing, and mobile technology creates new opportunities for organizations to incorporate AI into everyday business processes. Running trained models directly on devices can improve responsiveness and allow applications to perform certain functions without maintaining a constant cloud connection. At the same time, cloud platforms can continue collecting appropriate data and refining models to improve performance over time.
As artificial intelligence becomes more deeply embedded in enterprise applications, organizations will increasingly need development environments that connect cloud-based training with efficient on-device execution. Enterprise machine learning platforms that simplify this process can help businesses deploy intelligent applications faster while taking advantage of both centralized computing resources and mobile hardware. This approach provides a foundation for more responsive, data-driven enterprise applications across a growing range of industries and use cases.
Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group
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