Building a machine learning model can be difficult, but that is only half of the battle. Having a perfect model is only useful if you are able to get it into production. In this episode Stepan Pushkarev, founder of Hydrosphere, explains why deploying and maintaining machine learning projects in production is different from regular software projects and the challenges that they bring. He also describes the Hydrosphere platform, and how the different components work together to manage the full machine learning lifecycle of model deployment and retraining. This was a useful conversation to get a better understanding of the unique difficulties that exist for machine learning projects.
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- Your host is Tobias Macey and today I’m interviewing Stepan Pushkarev about Hydrosphere, the first open source platform for Data Science and Machine Learning Management automation
- How did you get involved in the area of data management?
- Can you start by explaining what Hydrosphere is and share its origin story?
- In your experience, what are the most challenging or complicated aspects of managing machine learning models in a production context?
- How does it differ from deployment and maintenance of a regular software application?
- Can you describe how Hydrosphere is architected and how the different components of the stack fit together?
- For someone who is using Hydrosphere in their production workflow, what would that look like?
- What is the difference in interaction with Hydrosphere for different roles within a data team?
- What are some of the types of metrics that you monitor to determine when and how to retrain deployed models?
- Which metrics do you track for testing and verifying the health of the data?
- What are the factors that contribute to model degradation in production and how do you incorporate contextual feedback into the training cycle to counteract them?
- How has the landscape and sophistication for real world usability of machine learning changed since you first began working on Hydrosphere?
- How has that influenced the design and direction of Hydrosphere, both as a project and a business?
- How has the design of Hydrosphere evolved since you first began working on it?
- What assumptions did you have when you began working on Hydrosphere and how have they been challenged or modified through growing the platform?
- What have been some of the most challenging or complex aspects of building and maintaining Hydrosphere?
- What do you have in store for the future of Hydrosphere?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Data Engineering Podcast at ODSC
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- The Open Data Science Conference
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- Protocol Buffers
- TensorFlow Extended
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- Generative Adversarial Networks