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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- Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask.
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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