“The Monolith team understood what it means to work with genuine engineering problems in artificial intelligence: the needed flexibility and knowledge.”
“It's a team with a very good personal relationship.”
“The Monolith AI team is excellent.”
“The best thing about this software is the team. All the team members are experienced and always available to help and solve the issues”
“They are prompt with communication, well organized, really know their stuff, and are good at communicating with non-experts in their field.”
Finding the root cause of a system or test error starts with getting all of your data in order. When you are faced with inconsistent results from similar inputs, its time to expand your data set and look for new correlations. Using the data transformation tools in Monolith, you can convert your raw data into features or derivatives to find different relationships or gain more insight into the operation of your design.
Or, you may need to add more data to expand your field of vision. With Monolith's scalable cloud resources, you can add to your dataset and improve your models to learn more about possible reasons for failure.
Finding the root cause of a system or test failure starts with knowing the inner workings of your design. Even the most experienced engineers can supplement their inherent understanding of the products they work with AI models to learn more about their design and how it works.
With Monolith, you can build a more in-depth understanding of which inputs and conditions have a greater impact on the performance of your product. Using the advanced visualization tools in Monolith, you can quickly look across many different parameters and see how different values impact your test results to form more intuitive understanding of what is happening and where to prioritize your focus when dealing with failures.
Using advanced time-series auto-encoders, you can reconstruct your design based on test data to make better predictions on how it should perform under different conditions. Using these models, you can monitor test results in near-real time to predict the outcomes and stop tests early if the models predict a failure.
Identifying failures quickly and testing under different conditions help narrow down the options that could be causing issues. You can complex relationships and correlations with machine learning to move with greater agility and confidence when the pressure of failures mount.
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