Orchestrate AI Today

Union is designed with a focus on data ownership and control, as it is deployed into your AWS or GCP cloud account. Union uses an operator that manages your infrastructure in the data plane. While your code, data sets, and secrets reside here, Union does not have direct access to these. This architecture ensures that customers have full control over their data and resources, maintaining privacy, security, and compliance with organizational policies.

RBAC is a crucial feature for managing security and access within your organization. It allows you to assign permissions and control access to resources based on users' roles, ensuring that each team member has the appropriate level of access to perform their tasks effectively while maintaining data privacy and security.

Union’s multi-tenancy feature allows multiple users to share the same platform while maintaining their own unique data and configurations. This centralized infrastructure enables effective resource management and organization, while also facilitating seamless team collaboration within your organization.

“The multi-tenancy that Flyte™ provides is obviously important in regulated spaces where you need to separate users and resources and things like amongst each other within the same organization.”

Jake Neyer, Software Engineer at Striveworks

Set a specified cadence for your workflows by scheduling them to run at regular intervals. This ensures that your workflows execute automatically at the desired frequency, minimizing manual intervention and optimizing efficiency.

Data lineage provides an essential means to trace the origin of errors within your workflows. By monitoring the data's journey and transformations across the entire lifecycle of your workflows, it becomes easier to identify the source of any issues. This efficient approach to debugging and troubleshooting saves time and resources, enabling rapid resolution of problems as they occur.

Enable comprehensive visualization throughout every step of your workflow to visualize your data, monitor your models, and view training history through plots.

Experience enhanced task-level monitoring with Union, empowering entire teams with valuable insights to optimize their workflows. By reducing task execution time and providing system feedback comparable to a local environment, Union enables a more efficient and streamlined experience. Benefit from detailed, individualized data rather than just aggregated information, allowing for more precise analysis and informed decision-making across your organization.

Visualization makes data easier to comprehend. Union provides first-class support for rendering data plots.

Ensure the integrity of your data throughout your workflow by establishing data guardrails. This will prevent any data errors from slipping through the cracks, while also allowing your workflow to remain informed of how the data evolves at each step.

Schedule your tasks to run on GPUs. Leverage the power of GPU processing, providing faster execution times and enhanced performance for ML and data-intensive workloads.

Tasks are inherently parallel to optimize resource consumption and improve performance, so you don't have to do anything special to enable parallelism.

Signaling allows manual actions to influence the course of a workflow. This allows a human to potentially intercept a workflow and either redirect or approve the tasks. This is helpful for labeling, supervised learning, and data curation.

Task boundaries provide natural checkpoints for your workflow, but in certain scenarios, such as training a model, they can be expensive. Training can be both time-consuming and resource-intensive, making it critical to ensure that progress is regularly saved. Intra-task checkpoints provide a solution by allowing you to checkpoint progress within a task execution, minimizing resource waste and optimizing performance.

Debugging can be a costly and time-consuming process, especially when it involves rerunning previously successful tasks. Optimize your workflow's efficiency by utilizing the recoverability feature, which allows you to selectively rerun only the failed tasks and conserve both time and resources.

Efficiently debug issues within your workflow by rerunning it at the most granular level, without altering the previous state of any data or ML workflows. This allows you to quickly pinpoint and address issues.

Optimize your workflow's execution time by caching task outputs. When the task signature remains unchanged, the cache skips the need to rerun any long-running executions, preventing unnecessary resource wastage and significantly speeding up your workflow's execution.

Immutable executions are critical for ensuring reproducibility, as they prevent any changes to the state of execution. This provides the flexibility to completely restructure a data or ML workflow between versions without fear of any negative impact on production. With immutable executions, you can confidently iterate and experiment while maintaining the integrity of your data and workflow.

“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.”

Jeev Balakrishnan, Software Engineer at Freenome

Take advantage of spot instances with ease by scheduling your workflows to run on them and significantly reduce your costs. Effortlessly optimize your workflow's efficiency while minimizing expenses.

“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.”

Varsha Parthasarathy, Senior Software Engineer at Woven Planet

“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.”

Calvin Leather, Staff Engineer and Tech Lead at Embark Veterinary

Ensure reliable task completion by setting timeouts. Timeout allows you to specify a maximum amount of time for a task to run, ensuring that the task always completes within a specified timeframe.

Dynamic resource allocation is a key feature in optimizing workflow efficiency. With Union, resources required for a task can be adjusted on the fly based on user-provided inputs or real-time calculations. This adaptability ensures that your tasks have the necessary resources to run efficiently, enhancing overall performance and resource utilization.

Stay up-to-date on your workflow's state by configuring notifications through popular platforms such as Slack, PagerDuty, or email. Receive valuable real-time updates and alerts to quickly address any issues that may arise and maintain control over your workflow's performance

Compose your workflows in the language that best suits your team's expertise, with support for both SDK and raw containers. This flexibility allows you to write workflows in any programming language you prefer.

Data and ML practitioners require the ability to experiment and iterate without disrupting their workflow. With versioning, they can work in isolation and reproduce their results, as well as rollback to a previous version of their workflow at any time. This provides the necessary flexibility to experiment and iterate with confidence.

“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.”

Pradithya Aria Pura, Principal Software Engineer at Gojek

Different tasks within a workflow may have varying resource requirements and library dependencies. Without careful management, this can cause conflicts that can negatively impact your workflow's performance. Union lets you maintain separate sets of dependencies for your tasks, ensuring that no conflicts arise and that your workflow runs smoothly and efficiently.

“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.”

Bernhard Stadlbauer, Data Engineer at Pachama

Union’s multi-cloud support enables users to seamlessly utilize multiple cloud providers (current support for AWS and GCP with more to come), including on-premises infrastructure if requested. This flexibility allows organizations to choose the best cloud solution that suits their specific requirements and preferences, ensuring a streamlined, adaptable, and efficient workflow experience regardless of the underlying infrastructure.