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Operationalization of Data Science (MLOps)

Maximize the benefits of AI by integrating AI processes with business processes and ensure data quality and traceability of the AI process.Contact us now

Key challenges that MLOps overcome

Trust in data and the process behind it is key to the successful use of Artificial Intelligence. However, this poses numerous challenges for companies:

Assessment

Unambiguous assessment of the origin and correctness of the data for the decision

Requirements

Duration and effort for the operational deployment of a new algorithm must be lean

Rating

Evaluation of the technical correctness of the algorithm

Quality

Embedding AI in the business process requires quality: documented, monitored, traceable

Are you facing similar challenges?

AI platforms help you provide documented, traceable information and implement MLOps. Generate intelligent AI processes from trusted data and embed them into business processes to make informed decisions. We support you in this!

Advantages of an AI platform:

MLOps' challenges in handling data and the process behind it can be overcome with an AI platform:

  • 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

Cloud Pak for Data as AI platform

Experience from previous customer situations have shown that the "Cloud Pak for Data" solution offered by IBM comprehensively supports the AI process incl. MLOps in all aspects. Graphically, the solution and the process look like this:

Graphic on Cloud Pak for Data as an AI platform

Practical example with our customers


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. 

Our Services:

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.

Proof-of-Value Workshops

Determination of data situation and use cases as feasibility study incl. proposal of target architecture for AI project

Introduction & Implementation

Identify open challenges in existing or new AI projects and the potential benefits of adopting an AI platform.

Selection & Implementation

Support in the selection and implementation of an AI platform

Support

Support for the introduction of AI and AI platforms

Contact us now!

We would be happy to advise you in a non-binding conversation and show you how you can benefit from MLOps. Just leave your contact details and we will get back to you as soon as possible.

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Feel free to contact us!

David Brockschmidt
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Marc Bastien
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