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
Join our developer community
“Gojek is experiencing rapid growth and incorporating machine learning into various products. To sustain this growth and guarantee success, a reliable and scalable pipeline solution is critical. Flyte plays a vital role as a key component of Gojek’s ML Platform by providing exactly that.”
“We got over 66% reduction in orchestration code when we moved to Flyte™ — a huge win!”
“Union Cloud solves our operational complexity problems across diverse workloads, whether it is running data cleaning & pre-processing workflows or protein structure ML predictions for low-volume, high-complexity scientific workloads to large-scale scientific simulations. Additionally, the platform can drive down the relative cost of protein production by orders of magnitude. With Union Cloud as our standardized workflow orchestration platform, we can stop managing our own systems and infrastructure, and instead focus on antibody discovery and development.”
“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.”
“Because we are a spot-based company, a lot of our workflows run into the majority of issues. Thankfully, with Flyte™, we can debug and do quick iterations.”
“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.”
“FlyteFile is a really nice abstraction on a distributed platform. [I can say,] ‘I need this file,’ and Flyte™ takes care of downloading it, uploading it and only accessing it when we need to. We generate large binary files in netcdf format, so not having to worry about transferring and copying those files has been really nice.”
“Given the scale at which some of these tasks run, compute can get really expensive. So being able to add an interruptible argument to the task decorator for certain tasks has been really useful to cut costs.”
“With Flyte™, we want to give the power back to biologists. We want to stand up something that they can play around with different parameters for their models because not every … parameter is fixed. We want to make sure we are giving them the power to run the analyses.”