Why Open Source beats Proprietary Software for MLOps

Scales

Machine learning teams can use proprietary platforms like Knime or SageMaker, or build their own using open source tools. Companies often sell proprietary platforms as more powerful, more efficient, and easier to use. But in reality, they’re often more complicated and less powerful than open source alternatives. We build our own MLOps architecture from purely open source foundations. Here’s why ownership of your platform is important.

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Biologists and Data Scientists: The cultural divide

The fields of biology and data science have a lot in common. Data scientists and biologists both analyze datasets to try to make sense of the world. Today, data science is becoming increasingly important for biology, as biologists increasingly use machine learning and AI for drug discovery, medical diagnosis, and automating repetitive tasks.

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Detecting COVID-19 outbreaks through wastewater monitoring

Testing individual members of the population at scale is expensive and logistically difficult: limited laboratory equipment and medical facilities make it almost impossible to test an entire country’s population. A cheaper and logistically simpler strategy is to analyze sewage. Because COVID-19 and other viruses can be detected in human excrement, wastewater treatment plants offer aggregated information.

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