Trust in data and the process behind it is key to the successful use of Artificial Intelligence. However, this poses numerous challenges for companies:
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Operationalization of AI processes
Using Artificial Intelligence to gain insights from data is already a great advantage. This advantage can be multiplied a hundredfold if the AI process is embedded in the actual operational process and can thus constantly deliver its added value. This in turn requires the AI process to operationalize itself: AI must be available as a service, via REST API, as a batch job.
Provisioning and monitoring of services
The provision of AI services in the required form must be agile, performant and traceable. The same requirements apply as for all other business-critical processes.
Long-term technical and business monitoring (BIAS) of AI models.
AI processes are subject to a constant learning and aging process, as the underlying business process is also evolving. A progression in the operational process must be countered with an evolution of the AI process and this must be automated as much as possible. Likewise, potentially undesirable influences ("BIAS") must be detected and documented.
Data cataloging/governance
Central cataloging of data enables faster and more agile development of AI processes. When the origin and meaning of data is documented, AI can make the right predictions more safely and reliably.
Data Virtualization
Making data available to the AI process can be a challenge. Data virtualization can help by making data available to the AI process not physically, but virtually, fully documented and protected.
In our projects, we analyze the individual requirements based on the customer's situation. Then, on the basis of technical and business requirements, we suggest the appropriate architecture to achieve the goals and implement it if necessary.
In the AI platform, thanks to "AutoAI", a model equal in quality and grade was created with a few clicks and a usable, monitored AI process was created in a few minutes. Using OpenScale, this process is now monitored by AI for quality, drift and BIAS.
Marc BastienSoftware ArchitectTIMETOACT
Marc Bastien, Software Architect at TIMETOACT, will show you how IBM Cloud Pak for Data and structured application of Data Science identifies and exploits untapped potential in processes using project examples from our customers.
The breadth and simultaneous depth in covering all analytical issues is TIMETOACT's strength. Experience from decades of consulting and close cooperation with the leading technology providers benefits every project. TIMETOACT has always been at the forefront of adapting new, modern solutions from the technology leaders.
