for Machine Learning

Streamline Machine Learning, Not Tool Collections

Building ML pipelines takes more than just training, testing and deploying models. You need accuracy, scalability, reliability and efficiency throughout the process. Union Cloud, a managed version of the Flyte™ workflow orchestrator, ensures flawless, efficient modeling and MLOps workflows.

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“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.”

— Jake Neyer, Software Engineer at Striveworks

How ML orchestration works

Machine Learning Orchestration is a game-changer for scalable machine-learning workflows and pipelines. These systems need to manage computing resources far more efficiently than data orchestrators and rely on features like check-pointing and visualization. ML orchestrators support the dynamic demands of ML development, enabling seamless implementation, management and deployment of production-grade ML pipelines, ultimately simplifying the creation of ML-powered products.

1. Explore

Conduct Exploratory Data Analysis (EDA) to gain dataset insights, identify patterns and relationships, and determine the most suitable techniques and tools for your machine learning project, such as natural language processing, computer vision and reinforcement learning.

6. Retrain

Retraining ML models is crucial to maintain their accuracy and relevance. It entails updating models with new data, insights or improved algorithms; adapting to changing conditions or user needs; and addressing concept drift.

5. Monitor

This component tracks model performance, health, and data and concept drift over time to identify the ideal moment to retrain or update a model. Tools like Grafana, Prometheus and Arize are instrumental in monitoring ML models.

Modeling

2. Create

ML models are trained on features, a process that demands significant computing power and can be repeated multiple times to develop an optimal model. To train models, you need frameworks like PyTorch and TensorFlow.

3. Test

Deployment ensures ML models are regularly updated or re-trained to manage dependency, optimize performance and scale horizontally. Specialized tools address specific aspects of deployment, such as Docker for dependency management and BentoML for serving models.

4. Deploy

ML orchestration coordinates all these pipeline components end to end, providing critical features such as scheduling, versioning, resource management, dynamic workflows and caching. Orchestrators like Flyte™ or Kubeflow Pipeline offer ML pipeline orchestration, so teams can focus on building ML-powered products.

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Limitless: Union Cloud powered by Flyte™

Flyte™ is a machine-learning orchestrator that enforces an architectural design pattern for data and ML workflows. Built on top of Flyte™, Union Cloud lets ML engineers and data scientists focus on their work instead of managing infrastructure. Union Cloud with Flyte™ makes it easy for data scientists and ML engineers to focus on their ML and model pipelines.

Infinitely scalable

Union Cloud lets you effortlessly expand your ML workflows, capitalizing on its virtually unlimited capabilities for growth and adaptability.

Built for scale

Union Cloud employs the Kubernetes-native Flyte™ engine — designed to handle extensive ML operations — to optimize performance even of large-scale deployments.

Language agnostic

Union Cloud provides data scientists and ML engineers the flexibility to compose workflows in their preferred programming languages, fostering seamless collaboration and innovation.

Versioned workflows

Union Cloud's versioned workflows preserve the history of your runs, empowering you to track progress and easily revert to previous iterations when necessary.

Multi-tenancy

Union Cloud's multi-tenant support lets multiple teams collaborate on a unified platform, promoting efficient knowledge sharing and streamlined teamwork.

Data lineage

Union Cloud enhances end-to-end data observability by tracking data lineage, ensuring that you have a clear understanding of data transformations and dependencies throughout the ML pipeline.

Strong typing

Union Cloud incorporates robust compile-time checks to bulletproof your workflows, reducing the likelihood of errors and increasing overall reliability.

Parallelism

Union Cloud's inherent parallelism reduces wait times for your workflows, equipping you to execute tasks concurrently and complete projects more quickly.

Maximal efficiency

Union Cloud ensures that you don't have to rerun workflows if assumptions remain unchanged, optimizing resource usage and promoting maximal efficiency.

Infrastructure managed

Union Cloud is responsible for provisioning and managing your Kubernetes cluster and resources. This allows users to scale workflows and data planes with ease.