BioTech

As a discipline, Life Sciences has long been at the forefront of data science. Now, machine learning (ML) and artificial intelligence (AI) adoption is surging in the Biotech industry. But modern Biotech has complex and expensive R&D requirements. Biotech firms must abide by an independent regulatory process to ensure the safety and efficacy of any findings as well.

Now, data scientists are struggling to keep up with competitors and business demands. They are juggling scripts and analyses instead of innovating and using technology as a force-multiplier for business. “We had a lot of artisanally crafted, one-off ad hoc analysis,” says Mauricio Borgen, Director of IT & Scientific Compute at Agbiome.

Bench scientists know that the scientific process requires result to be reproducible in order to be accepted. But despite their best efforts, data scientists often end up using one-off workflows that are not reproducible at all. This raises R&D costs, delays regulatory processes, and prevents new biotech solutions from getting to the market.

How Pachyderm can help

We know first hand how to help biotech companies do data science better. In the case of Agbiome, Pachyderm helped automate tasks so they can be completed more quickly, affordably, and accurately than before. What truly sets Pachyderm apart is our unique ability to provide data lineage with iterative, easy-to-assemble pipelines. And with Pachyderm, data scientists can use and succeed with whatever languages and frameworks they choose.

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