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.
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- 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 Yaron Haviv about Iguazio, a platform for end to end automation of machine learning applications using MLOps principles.
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Oracle Exadata
- SAP HANA
- Multi-Model Database
- Jupyter Notebook
- Feature Imputing
- Feature Store
- Apache Flink
- Apache Beam
- NLP (Natural Language Processing)
- Deep Learning
- AWS Step Functions