From Chaos to Scale — Orchestrating AI Pipelines with Flyte

Since the term big data first introduced in the early 1990s, the volume of global data has grown at an exponential pace. As machine learning continues to rise, companies are building countless AI pipelines to solve real-world business problems, from predicting ad clicks to personalizing recommendations.

At its core, an AI pipeline can be thought of as:

But in practice, building and managing these pipelines at scale is far from simple. As companies grow, two major problems emerge: deploying across multiple clusters becomes a nightmare, and pipelines break constantly in production.

To address these pain points, Lyft created Flyte [3], an open-source orchestration platform designed to support tens of thousands of AI pipelines at scale.

1. Multi-cluster deployment - Reliably ship code to production at scale, without endless manual fixes
   • 🤕 Without Flyte:
     • Users need to manually set up clusters for different domains (e.g. development and production).
     • Extra care should be taken to move workflow between domains and clusters
     • Manual intervention is needed when nodes fail.
   • 😄 With Flyte:
     • Users can simply execute the workflow with --domain argument specifying “development” or “production”.
     • Flyte automatically determines where to run the code for you.
     • If any node failed, Flyte will automatically transfer the workflow to other available nodes.
2. Robust pipeline - Build resilient workflows that handle failures gracefully and ensure data integrity
   • 🤕 Without Flyte:
     • Runtime type errors crash production pipelines.
     • Inconsistent environments cause “works on my machine” issues.
     • Tracking the exact code version that produced results can be difficult.
   • 😄 With Flyte:
     • Strong typing catches errors before deployment.
     • Containerized tasks ensure identical environments.
     • Immutable workflow versions guarantee reproducibility.

Features

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Flyte tackles five specific challenges that AI teams commonly face:

1. Scalability - Handle massive growth in requests and data with efficient concurrency
   • Built on Kubernetes, Flyte automatically scales workflows across clusters and handles node failures without manual intervention.
2. Reusability - Reuse components across pipelines and business units, avoiding “reinventing the wheel."
   • Centralized tasks and workflows components management across teams with cache support to save time and compute resources
3. Reproducibility - Ensure experiments can be replicated by mirroring dev environments in production
   • Versions all components with packages and resources set, which guarantees result consistency across development and production environments
4. Maintainability - Operate and update thousands of pipelines with ease
   • Simple SDK interface separates workflow management from execution, allowing users to define workflows once in a central location and run them across multiple clusters

5. Extensibility - Integrate smoothly with third-party tools and services
   • Connects with popular data platforms like Databricks, Snowflake, and AWS Batch while providing centralized workflow management, monitoring, and error handling

Today, Flyte is trusted by leading companies such as Tesla, Spotify, LinkedIn, and Toyota, and runs over 30 million tasks per day. For example, in a recent LinkedIn Engineering blog [4], the team unveiled their next-generation AI pipelines powered by Flyte, achieving 20–30× faster training and launch times, while enabling them to train models 200× larger than before.

How to Run a Workflow in Flyte?

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The Flyte workflow can be defined through simple Python syntax that is similar to other workflow orchastration tools.

import flytekit as fl


@fl.task
def say_hello(name: str) -> str:
    return f"Hello, {name}!"


@fl.workflow
def wf(name: str = "world") -> str:
    greeting = say_hello(name=name)
    return greeting

After defining the workflow, we can either executing it locally by pyflyte run task.py wf for debugging locally or running on remote with pyflyte run --remote task.py wf. When running on remote, Flyte automatically creates Kubernetes pods to execute tasks and provides real-time status updates.

The command prints a URL to access Flyte Console, a web dashboard for monitoring workflow progress, viewing logs, and managing operations like retries.

❯ pyflyte run --remote hello_world.py wf
Running Execution on Remote.

[✔] Go to http://localhost:30080/console/projects/flytesnacks/domains/development/executions/a465495htfhprw5fhp2w to see execution in the console.

References

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[1] Amount of Data Created Daily (2025)
[2] A Chat with Andrew on MLOps: From Model-centric to Data-centric AI
[3] Flyte OSS
[4] OpenConnect: LinkedIn’s next-generation AI pipeline ecosystem
[5] On Orchestrators: You Are All Right, But You Are All Wrong Too