Python 2’s end-of-life announcement meant that we had to move a very large codebase to Python 3. Here’s how we successfully did it in a matter of days, and when our approach may work for you for similarly large projects.
A followup to our previous discussion of running parallel tasks in Celery, with details on how we solved the largest problem with our initial implementation.
My job as a biologist in the lab used to consist of moving liquids back and forth by hand. At Zymergen, I’m training robots to automate the worst parts of that job, and I’m here to tell you why you should look forward to fully automated luxury science.
Our end-to-end approach for model selection for unsupervised outlier detection algorithms, with an emphasis on parameter tuning.
The World Economic Forum and McKinsey recently published a whitepaper in which they recognize Zymergen as a “lighthouse company”, helping drive what they characterize as the Fourth Industrial Revolution (they also presented us with a lighthouse award, which a number of my colleagues shared from Davos).
No one would sell us a high-throughput lab automation system that could be reconfigured in minutes and would scale to fill a factory. So we built one.
An introduction to running parallel tasks with Celery, plus how and why we built an API on top of Celery’s Canvas task primitives.
How solutions engineering goals drive our architecture design.
At Zymergen we are building a data science ecosystem where machine learning algorithms help predict which genetic edits will produce strains with enhanced chemical production.
We’re proud of our team’s culture. As we grew, we found we wanted to write down the values that have created that culture. In this post we introduce the Zymergen Tech Team values, share the method we used to develop them, and provide additional commentary which we hope captures our thoughts and intentions behind each value.