We're building the first machine learning operations (MLOps) platform specifically designed for the biotechnology industry. We understand the challenges of designing, implementing, debugging, and deploying models that run on biochemical data from first-hand experience. Now, we're using what we've learned to build MLOps tools loved by chemists, machine learning engineers, and team leads alike.
A free, open-source toolkit to help chemists and machine learning engineers easily input, analyze, and split chemical data sets.
Linear algebra is the language of machine learning. Unfortunately, matrices and tensors can obscure your training data, making it hard to debug models that underperform. We're building tools that seamlessly translate between the data representations familiar to scientists (i.e. SMILES strings and protein structures) and those required by ML engineers.
Building a useful ML model in biotech can require expertise in everything from medicinal chemistry and structural biology to software engineering. Unfortunately, existing debugging tools do not work well for these multidisciplinary teams. We're making it easy for ML engineers to collect domain knowledge from experts and incorporate it into their model design, preventing wasted money and time.
Sharing ML models usually consists of sending a jumble of scripts, debugging environment errors for hours, and then using a hacked-together user interface to generate mission-critical predictions. We believe that ML models should be easy for engineers to deploy and for scientists to use. That means building on industry-standard tools like Git to deploy models and generating flexible, intuitive interfaces to quickly employ the model.
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