Solution: AI Operations

Solution: AI Operations

Kian Buchsbaum

Ferdinandstraße 4/1.OG 1020 Wien Österreich

+43 1 375 75 00 14

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DI Alexander Zeppelzauer

Region: Österreich

TÜV AUSTRIA-Platz 1 2345 Brunn am Gebirge Österreich

+43 664 60454 6276

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TÜV AUSTRIA-Platz 1 2345 Brunn/Gebirge Austria

+43(0)504 54

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AI Operations

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"AI Operations bezieht sich auf die Verwendung von KI zur Verbesserung und Automatisierung verschiedener Aspekte des IT-Betriebs und der IT-Verwaltung."

What does AI Operations mean?

AI Operations, often abbreviated as AIOps, refers to the use of artificial intelligence and machine learning to improve and automate various aspects of IT operations and management, such as monitoring, troubleshooting and incident response. Reliable AI Operations helps companies to improve the efficiency and reliability of their IT systems.

The development of AI solutions is often of a research nature. That means that the results are not designed for long-term operation. There is often a lack of monitoring of the models in terms of minor quality, drift, etc. Conversely, there is also no alerting system that warns the user of risks, such as uncertain model predictions. Neither can the business case be checked nor can damage caused by model errors be prevented. In addition, businesses can easily lose models and the associated know-how if an employee leaves the team.

What can AI Ops offer?

AI Operations offers the development of a platform solution (in AWS, Azure or onPremise) that ensures the operation of models.
We start with an ML governance project in which we record all of the company’s relevant AI systems and document them using model cards.
We host the models in the cloud or in their on-premise environments and set up a monitoring system supported by BI tools that give you direct insights into the performance of your models. In addition to monitoring, we set up an alerting system to warn the model owner of unwanted behavior (e.g. data drift, model drift). We also offer continuous model re-training. This involves fully integrating the models into a platform. Such a platform offers the possibility to train models in an audit-proof manner and to monitor both model and data quality.

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