/ Flyte features

The modern framework for orchestrating complex, data-intensive workflows

By combining a powerful compute backend with an elegant Pythonic interface, Flyte brings software engineering best practices to every step of the AI lifecycle, enabling teams to build resilient, reproducible pipelines.

Built with AI in mind

Flyte was built from first principles to solve the unique challenges presented by AI development. Containerized workloads, automatically-versioned entities, and data management abstractions ensure that work can be reproduced and seamlessly promoted from development to production. Native compute and first-class integrations with external systems allow for fast, efficient distribution of workloads across a shared backend. An API-driven architecture promotes interoperability and reuse of data and model artifacts across the organization.

Built for rapid iteration

Flyte was designed to help AI developers rapidly prototype, test, and ship complex, data- and compute-intensive workflows. Single-task executions, image management in code, and declarative infrastructure allow workflow authors to incrementally develop pipelines one step at a time. Local-remote parity enables teams to test workflows in CI while running the same logic at scale in remote environments. Task and workflow composability and dynamism support complex AI-specific use cases such as hyperparameter tuning.

Production-ready resiliency

Flyte was conceived by a team of distributed systems experts to provide extreme failure resiliency and ease of debugging. Caching and automatic recovery facilitate self-healing workflows. Type safety and error-driven branching increase the probability that a given workflow succeeds. Native multi-tenancy and deep integration with IAM ensure secure yet efficient sharing of resources.

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