Bring your models to production and start reaping business value by leveraging Lifely’s extensive knowledge and experience with AI models in combination with the industry-leading AI hosting platform UbiOps.
məˈʃiːn ˈlɜːnɪŋ ˌɒpəˈreɪʃənz
The end-to-end machine learning development process, which includes designing, building and managing machine learning software, with a focus on reproducibility, testability and evolvability of infrastructures.
For any AI model, but especially for the ones that need to make it into production, we need to figure out what the exact use case will be. A computer vision model that has to do its job almost continuously and with speed requires a completely different architecture than a model that just crunches numbers and runs once a year. We will investigate your exact needs together so that you don’t pay a dime too much.
To make sure your models continuously improve, we set up a data pipeline so that your model will stay up-to-date, without manual labour. We set up data versioning to make sure you are always in control.
Most AI models are overengineered. The biggest difference between models in production and prototyped ones is their optimization goal. Prototypes are often optimized for certain performance metrics, such as accuracy. Models in production should be optimized for their minimal acceptable performance, which is the level from which the model delivers real-world value. This minimal acceptable performance can be reached with distilled and optimized models, or models with a less complex structure, saving you both computational time and money when bringing the model into production.
UbiOps provides a stable fundament to work with. Their platform offers efficient scaling while giving you control. This way, we can solve bringing models into production for good, offering you longevity of your models and fostering your internal capabilities to handle MLOps in the future.
Our last step is making sure you know exactly what’s happening. The world around us changes, and so can the effectiveness of your model. We always end our MLOps processes by defining clear metrics for you to keep track of the performance of your models, so you’ll know when to act.
Organizations that successfully brought AI models into production saw on average a 3% to 15% increase in profit margin, but up to 88% of all organizations are struggling to move these models past testing stages. Keep your implementation cycles short and focus on providing value in production before diving into the next project.
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