The life sciences as an industry has seen incredible growth in scale and sophistication, along with the advances in data technology that make it possible to analyze massive amounts of genomic information. In this episode Guy Yachdav, director of software engineering for ImmunAI, shares the complexities that are inherent to managing data workflows for bioinformatics. He also explains how he has architected the systems that ingest, process, and distribute the data that he is responsible for and the requirements that are introduced when collaborating with researchers, domain experts, and machine learning developers.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Guy Yachdav, Director of Software Engineering at Immunai, about his work at Immunai to wrangle biological data for advancing research into the human immune system.
Interview
Introduction (see Guy’s bio below)
How did you get involved in the area of data management?
Can you describe what Immunai is and the story behind it?
What are some of the categories of information that you are working with?
What kinds of insights are you trying to power/questions that you are trying to answer with that data?
Who are the stakeholders that you are working with and how does that influence your approach to the integration/transformation/presentation of the data?
What are some of the challenges unique to the biological data domain that you have had to address?
What are some of the limitations in the off-the-shelf tools when applied to biological data?
How have you approached the selection of tools/techniques/technologies to make your work maintainable for your engineers and accessible for your end users?
Can you describe the platform architecture that you are using to support your stakeholders?
What are some of the constraints or requirements (e.g. regulatory, security, etc.) that you need to account for in the design?
What are some of the ways that you make your data accessible to AI/ML engineers?
What are the most interesting, innovative, or unexpected ways that you have seen Immunai used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working at Immunai?
What do you have planned for the future of the Immunai data platform?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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