🎉 AI Engineers: Aporia's 2024 Benchmark Report and mutiSLM has been released. View the report here>>

2024 AI & ML Report

Evolution of Models & Solutions

The process of setting up production ML environments and ensuring effective monitoring and observability is incredibly challenging in itself, before even reaching the stage of tackling other challenges relating to deploying new models.

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December 23


Real Time

Of ML practitioners agree that real-time observability is crucial for the success of ML models in production

There’s a clear consensus among most ML engineers – regardless of their industry or team size – that real-time observability is a crucial requirement for the success of ML models in production, because without it, they are oblivious to any issues that may occur.

Production Issues

Of ML engineers encounter issues related to
production models
on a daily/weekly basis

The fact that 93% of the respondents encounter issues with their production models on a daily/weekly basis highlights just how important it is to monitor and identify issues quickly, because a high volume of production issues can have financial implications for the business.


Of respondents report that their Large Language Models (LLMs) exhibit signs of hallucinations

With the increasing integration of LLM-based generative AI in enterprises, a major challenge arises: hallucinations, where AI produces inaccurate or nonsensical responses. This issue underscores the urgent need for solutions to ensure the accuracy and reliability of AI-generated content, essential for preventing reputational and financial harm.

1 month
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Observability isn’t just a tool for monitoring ML production issues, it also helps companies understand the effectiveness and impact of ML models from a use - case perspective.


The top challenges respondents face with

their current ML monitoring system


Setup and maintenance


Integration with existing tools


Limitations in providing value quickly

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