Infrastructure for AI, ML & Data
For developers managing AI, ML, and data workflows in production, the challenges extend well beyond scheduling and orchestrating DAGs. Union.ai addresses these complexities by offering a comprehensive infrastructure management platform designed for the nuances of such environments.
Union optimizes resources across teams and implements cost-effective strategies that can reduce expenses by up to 66%. Moreover, it's engineered to fit within your own cloud ecosystem, ensuring a robust and tailored infrastructure that scales with your technical demands.
Powerful DAGs, observability & cost-efficient engineering
Union is a fully-managed Flyte platform deployed in your VPC that provides a single-endpoint workflow orchestration and compute service to engineers building data and ML products.
Get built-in dashboards, live-logging, and task-level resource monitoring, enabling users to identify resource bottlenecks and simplifying the debugging process, resulting in optimized infrastructure and faster experimentation.
AI engineering for engineers
Union is an open AI orchestration platform that simplifies AI infrastructure so you can develop, deploy, and innovate faster. Unlike popular—but simple—AI engineering orchestrators, Union wrangles the infrastructure setup and management as well.
Write your code in Python, collaborate across departments, and enjoy full reproducibility and auditability. Union lets you focus on what matters.
Purpose-built for lineage-aware pipeline orchestration
Bring your own Airflow code (BYOAC) and take advantage of modern AI orchestration features—out of the box! Get full reproducibility, audibility, experiment tracking, cross-team task sharing, compile-time error checking, and automatic artifact capture.
Easily experiment and iterate in isolation with versioned tasks and workflows.
A centralized infrastructure for your team and organization, enables multiple users to share the same platform while maintaining their own distinct data and configurations.
Strongly typed inputs and outputs can simplify data validation and highlight incompatibilities between tasks making it easier to identify and troubleshoot errors before launching the workflow.
Caching the output of task executions can accelerate subsequent executions and prevent wasted resources.
As a data-aware platform, it can simplify rollbacks and error tracking.
Immutable executions help ensure reproducibility by preventing any changes to the state of an execution.
Rerun only failed tasks in a workflow to save time, resources, and more easily debug.
Enable human intervention to supervise, tune and test workflows - resulting in improved accuracy and safety.
We manage the infrastructure so you can build what matters
Union is the AI orchestration and infrastructure platform of choice for many top data and ML teams globally. Esteemed companies such as Woven Planet and AbCellera have transitioned their workflows from Airflow or Kubeflow to Union.
Union is up to 66 percent more cost-efficient with your compute resources, solves complex infrastructure challenges, and is built for rapid iteration across teams.
Globally trusted & tested
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“Flyte has this concept of immutable transformation — it turns out the executions cannot be deleted, and so having immutable transformation is a really nice abstraction for our data-engineering stack.”
“When you write Python scripts, everything runs and takes a certain amount of time, whereas now for free we get parallelism across tasks. Our data scientists think that's really cool.”
“One thing that I really like compared to my previous experience with some of these tools: the local dev experience with pyflyte and the sandbox are super, super nice to reduce friction between production and dev environment.”
“We’re going to have 10,000-plus CPUs that we plan to use every day to process the raw data. There’ll be 30 different targets approximately that we’re collecting data on every day. That’s about 200 GB of raw data and probably 2 TB or so on the output — a lot of data process. We’re leaning heavily on Flyte to make that happen.”
“We get a lot of reusable workflows, and Flyte™ makes it fairly easy to share complex machine learning and different dependencies between teams without actually having to put all the dependencies into one container.”
“One of the biggest reasons we picked Flyte™ was because it is ideologically aligned with what we think all MLOps systems should have: strong lineage guarantees.”
“You can say, ‘Give me imputation’ and [Flyte™ will] launch 40 spot instances that are cheaper than your on-demand instance that you're using for your notebook and return the results back in memory.”
“Workflow versioning is quite important: When it comes to productionizing a pipeline, there are only a few platforms that provide this kind of versioning. To us, it's critical to be able to roll back to a certain workflow version in case there is a bug introduced into our production pipeline.”
“Our provisions time is at least two times faster on average. The model execution time, we have three times faster, and the cost, which we can actually probably further optimize with minimal configuration and optimization, is at least three times cheaper. And so over time, we’re expecting to save even more.”