E77: Simplify Your ML Infrastructure With Aqueduct

Open Source Shift

Aqueduct's transition to open source was not initially obvious, as Vikram Sreekanti explains. Initially, they built a closed-source tool, but security concerns and the need for user feedback prompted the shift. Open source allows users to try the tool and provide feedback, accelerating development. Joey Gonzalez highlights the complexity of MLOps, emphasizing the need for simplicity and integration in the field.

It's almost like the problem with mlops is mlops. It's just there are so many solutions to every single piece of the pie.

--- Joey Gonzalez

This decision has helped Aqueduct prioritize features and integrate with larger data platforms like Spark 1 2.

   

Community Engagement

Engaging with the community is crucial for Aqueduct's growth. Vikram Sreekanti stresses the importance of having a dedicated team member to nurture community relationships. This role is vital for gathering feedback and ensuring the community feels valued. Content creation plays a significant role in this engagement, with different types of content attracting varied levels of interaction.

How do you communicate out to the community in terms of content? How do you nurture those relationships once they're in the community and reengage with them, garner feedback, all those types of things.

--- Vikram Sreekanti

Timothy Chen notes the challenge of appealing to both data scientists and ML engineers, highlighting the need for targeted communication strategies 3 4.