The initial situation
You have successfully implemented and commissioned and analytical projekt using AI.
Then what?
Don't just ‘let it run’. After an analytical project using AI has been successfully implemented and put into operation, it is important to continuously refine the maintenance and optimisation of the solution. This is crucial to maintain the accuracy and effectiveness of the AI models, as operating conditions, customer behaviour, market situations and other variables are constantly changing.
Imagine a ship on the ocean whose captain is constantly watching the compass and factoring in external factors like weather to get to the destination harbour. Just looking at the compass once at the beginning of the journey will not be enough to get to the safe harbour.
1. Adapt to changing operating conditions to maintain the accuracy and effectiveness of AI models.
For example, temperature, pressure, humidity and other variables can change over time, which can affect the performance of AI models. Through regular monitoring, companies can recognise anomalies early and take proactive measures to maintain the accuracy and effectiveness of their AI solutions. The same applies to customer behaviour, the market situation and competitors. Everything is in flux and so the model must be adapted to deliver good results in the long term.
Last but not least, new data sources can (and should) be fed into the analytical system to further improve the results. New products with new customers, new industrial systems that provide new data and new data from the public sector should be considered here.
2. Early detection and proactive response to unexpected events to minimise downtime and maximise productivity. This includes breakdowns, material fluctuations, natural events, regulatory changes in the market, etc. All of these have an impact on the accuracy of the analysis.
3. Ensuring safety and reliability in safety-critical environments by identifying potential risks and implementing appropriate measures. AI solutions are often used in safety-critical environments where failures or malfunctions can have serious consequences. Here, too, the landscape is changing dynamically, such as new vectors of attacks on target companies, innovative technologies that bring new risks or old vulnerabilities in the software are discovered.
How does Datasense Consulting help?
Our approach is commonly called ‘observability’ in the industry. We offer our customers our own monitoring solution to keep track of the performance of the systems and the accuracy of the predictions (in the case of predictive).
The main components are shown in the picture.
Good to know
Confidential data does NOT leave the customer's perimeter, only the system and data logs, which allow us to monitor and optimise the performance of the ML system.
Continuous monitoring is crucial to ensure long-term performance, security and competitiveness. By implementing Observability, companies can ensure that their AI solutions function optimally and meet constantly changing requirements.
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