Bridging The Gap Between Machine Learning And Operations At Iguazio

Summary

The process of building and deploying machine learning projects requires a staggering number of systems and stakeholders to work in concert. In this episode Yaron Haviv, co-founder of Iguazio, discusses the complexities inherent to the process, as well as how he has worked to democratize the technologies necessary to make machine learning operations maintainable.

Announcements

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Your host is Tobias Macey and today I’m interviewing Yaron Haviv about Iguazio, a platform for end to end automation of machine learning applications using MLOps principles.

Interview

Introduction
How did you get involved in the area of data science & analytics?
Can you start by giving an overview of what Iguazio is and the story of how it got started?
How would you characterize your target or typical customer?
What are the biggest challenges that you see around building production grade workflows for machine learning?
- How does Iguazio help to address those complexities?
For customers who have already invested in the technical and organizational capacity for data science and data engineering, how does Iguazio integrate with their environments?
What are the responsibilities of a data engineer throughout the different stages of the lifecycle for a machine learning application?
Can you describe how the Iguazio platform is architected?
- How has the design of the platform evolved since you first began working on it?
- How have the industry best practices around bringing machine learning to production changed?
How do you approach testing/validation of machine learning applications and releasing them to production environments? (e.g. CI/CD)
Once a model is in production, what are the types and sources of information that you collect to monitor their performance?
- What are the factors that contribute to model drift?
What are the remaining gaps in the tooling or processes available for managing the lifecycle of machine learning projects?
What are the most interesting, innovative, or unexpected ways that you have seen the Iguazio platform used?
What are the most interesting, unexpected, or challenging lessons that you have learned while building and scaling the Iguazio platform and business?
When is Iguazio the wrong choice?
What do you have planned for the future of the platform?

Contact Info

LinkedIn
@yaronhaviv on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Bridging The Gap Between Machine Learning And Operations At Iguazio

About this episode

Summary

Announcements

Interview

Contact Info

Parting Question

Links

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