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.
Download SurveyHighlights
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.
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.
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.
Challenges
The top challenges respondents face with their current ML monitoring system
Setup and maintenance
Integration with existing tools
Limitations in providing value quickly
Download The Full Report
Fill out the form and get access to our 2024 Report