Amazon Lightsail
The fastest path from AI concept to production with predictable cloud infrastructure.

The Kubernetes-native workflow orchestrator for scalable and type-safe ML and data pipelines.
Flyte is an enterprise-grade, cloud-native workflow orchestrator designed specifically for machine learning and complex data processing. Originally developed at Lyft to solve the challenges of massive-scale data processing, it has evolved into a cornerstone of the MLOps ecosystem. Built on Kubernetes, Flyte employs a unique 'strongly-typed' architecture, ensuring that data passed between tasks adheres to strict contracts, which significantly reduces runtime errors in production. Its control plane, FlytePropeller, is written in Go and functions as a Kubernetes Controller, allowing it to scale to millions of concurrent task executions with minimal latency. In the 2026 market, Flyte distinguishes itself from legacy orchestrators like Airflow by offering native support for versioning, memoization, and dynamic workflow graph generation. It enables data scientists to write complex logic in Python while the underlying platform handles infrastructure provisioning, fault tolerance, and multi-tenancy. Flyte's architecture facilitates seamless transitions from local development to massive distributed clusters, making it the preferred choice for organizations running high-stakes AI workloads that require absolute reproducibility and auditability.
Every task has defined input and output types, allowing for compile-time validation of the entire workflow DAG before execution.
The fastest path from AI concept to production with predictable cloud infrastructure.
The open-source multi-modal data labeling platform for high-performance AI training and RLHF.
Scalable, Kubernetes-native Hyperparameter Tuning and Neural Architecture Search for production-grade ML.
The enterprise-grade MLOps platform for automating the deployment, management, and scaling of machine learning models.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Tasks can be cached based on input signature and version, allowing subsequent runs to skip expensive computations.
Ability to generate a new execution graph at runtime based on the outputs of previous tasks.
Native support for map-tasks and distributed processing like Spark, Ray, and MPI within a single workflow node.
FlytePropeller manages the lifecycle of Kubernetes pods and CRDs directly, treating infrastructure as code.
Every registration is immutable and versioned, ensuring that past executions can be perfectly replicated.
Native isolation of projects and domains (development, staging, production) within a single cluster.
Processing genomic data requires thousands of parallel tasks with complex dependencies and massive data transfers.
Registry Updated:2/7/2026
Generate patient-specific reports
Detecting model drift and triggering retraining only when necessary to save costs.
Ensuring audit trails and data lineage for regulatory reports.