Cloud Native Data Orchestration For Machine Learning And Data Engineering With Flyte
Data Engineering Podcast
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Navigating Data Management in Machine Learning
This chapter explores the speaker's journey in data management at Lyft, detailing the challenges faced in building machine learning models and handling large datasets. It emphasizes the evolution of data orchestration tools, comparing established systems like Airflow to innovative solutions such as Flyte, and highlights the importance of streamlining workflows for machine learning deployments. The discussion also underscores the need for efficient management strategies to optimize resource use while maintaining a focus on business logic in data processes.
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Summary
Machine learning has become a meaningful target for data applications, bringing with it an increase in the complexity of orchestrating the entire data flow. Flyte is a project that was started at Lyft to address their internal needs for machine learning and integrated closely with Kubernetes as the execution manager. In this episode Ketan Umare and Haytham Abuelfutuh share the story of the Flyte project and how their work at Union is focused on supporting and scaling the code and community that has made Flyte successful.
Announcements
- 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 Ketan Umare and Haytham Abuelfutuh about Flyte, the open source and kubernetes-native orchestration engine for your data systems
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Flyte is and the story behind it?
- What was missing in the ecosystem of available tools that made it necessary/worthwhile to create Flyte?
- Workflow orchestrators have been around for several years and have gone through a number of generational shifts. How would you characterize Flyte’s position in the ecosystem?
- What do you see as the closest alternatives?
- What are the core differentiators that might lead someone to choose Flyte over e.g. Airflow/Prefect/Dagster?
- What are the core primitives that Flyte exposes for building up complex workflows?
- Machine learning use cases have been a core focus since the project’s inception. What are some of the ways that that manifests in the design and feature set?
- Can you describe the architecture of Flyte?
- How have the design and goals of the platform changed/evolved since you first started working on it?
- What are the changes in the data ecosystem that have had the most substantial impact on the Flyte project? (e.g. roadmap, integrations, pushing people toward adoption, etc.)
- What is the process for setting up a Flyte deployment?
- What are the user personas that you prioritize in the design and feature development for Flyte?
- What is the workflow for someone building a new pipeline in Flyte?
- What are the patterns that you and the community have established to encourage discovery and reuse of granular task definitions?
- Beyond code reuse, how can teams scale usage of Flyte at the company/organization level?
- What are the affordances that you have created to facilitate local development and testing of workflows while ensuring a smooth transition to production?
- What are the patterns that are available for CI/CD of workflows using Flyte?
- How have you approached the design of data contracts/type definitions to provide a consistent/portable API for defining inter-task dependencies across languages?
- What are the available interfaces for extending Flyte and building integrations with other components across the data ecosystem?
- Data orchestration engines are a natural point for generating and taking advantage of rich metadata. How do you manage creation and propagation of metadata within and across the framework boundaries?
- Last year you founded Union to offer a managed version of Flyte. What are the features that you are offering beyond what is available in the open source?
- What are the opportunities that you see for the Flyte ecosystem with a corporate entity to invest in expanding adoption?
- What are the most interesting, innovative, or unexpected ways that you have seen Flyte used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Flyte?
- When is Flyte the wrong choice?
- What do you have planned for the future of Flyte?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links
- Flyte
- Union.ai
- Kubeflow
- Airflow
- AWS Step Functions
- Protocol Buffers
- XGBoost
- MLFlow
- Dagster
- Prefect
- Arrow
- Parquet
- Metaflow
- Pytorch
- dbt
- FastAPI
- Python Type Annotations
- Modin
- Monad
- Datahub
- OpenMetadata
- Hudi
- Iceberg
- Great Expectations
- Pandera
- Union ML
- Weights and Biases
- Whylogs
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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