ENPICOMManage your ML lifecycle
Standardized and reproducible machine learning models are essential for data scientists. While having a well-organized, rapid database of annotated data and metadata, and the ability to quickly deploy models into active discovery efforts are crucial, these alone are not enough. Effective utilization of ML models in discovery also requires robust MLOps support to track, register, and classify these models systematically. ML lifecycle management fosters a structured environment where great models are born, maximizing impact on drug discovery.
Standardized, reproducible machine learning
Data scientists face challenges keeping track of their ML experiments: What parameters were used? What dependencies does the model have? How does the performance of this model compare to others? MLOps is the management of the machine learning lifecycle, which is essential to efficient and reproducible ML model development and deployment. In biologics discovery specifically, large datasets of data can be hard to obtain.
For this reason, models are often generated in an iterative way while the data improves incrementally. Moreover, data scientists can sometimes be isolated from lab scientists, physically or in terms of knowledge, leading to hurdles for data scientists in obtaining the most up-to-date data, and hurdles for lab scientists in consuming the ML models in their analyses.
Connecting lab and data scientists
The ENPICOM platform provides data scientists with a private and integrated MLOps environment, complete with an intuitive UI to track experiments, store artifacts, register models and compare results. By standardizing the deployment of ML models, the ENPICOM empowers data scientists to efficiently create and pick the best ML models and puts them in the hands of the lab scientists. It’s the best of both worlds.
Data scientists benefit from SDK/API access to up-to-date standardized data generated by lab scientists for training/validation purposes
Data scientists enjoy an MLOps framework, allowing them to manage their ML lifecycle
Lab scientists get frictionless access to served models from the UI or integrated in a workflow, for inference or fine-tuning
How does it work?
Let's take a look at an example use case where lab scientists in your company have a wealth of thermostability data on antibodies.
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Selecting the best candidates
Because the model interface is known, the lab scientists will not have to configure anything when running the model: the platform knows what metadata is used as input, and where to store the output of the model.
From the perspective of the lab scientists, they select their candidates, choose the newly available thermostability model from a list of models, and see the thermostability predictions become available as newly created metadata.
Leaders in scientific research choose ENPICOM
Power your ML journey with seamless collaboration between data and lab scientists
Contact us for a personalized demo or consultation.