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

The Pythonic framework for high-scale data science and MLOps orchestration.
Metaflow is a human-centric framework originally developed at Netflix to help data scientists build and manage real-life data science projects. Architecturally, it sits as a layer above infrastructure, abstracting away the complexities of cloud compute, storage, and orchestration. In the 2026 landscape, Metaflow has evolved into the industry standard for bridging the gap between local development and production-grade execution. It utilizes a DAG-based (Directed Acyclic Graph) structure where users define steps using simple Python decorators like @step and @batch. Its core strength lies in its 'content-addressed' data store, which automatically versions every piece of data produced by every run, enabling perfect reproducibility and effortless debugging. By integrating natively with AWS Step Functions, Argo Workflows, and Kubernetes, it allows teams to scale from a single laptop to massive GPU clusters without changing their code. The framework’s philosophy emphasizes developer productivity, allowing scientists to focus on modeling while Metaflow handles the 'plumbing' of infrastructure, dependency management, and state persistence.
Every variable assigned to 'self' in a step is automatically serialized and stored in a content-addressed data store (S3/Azure/GCS).
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.
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Developers use @resources(cpu=4, gpu=1, memory=16000) directly in Python code to provision cloud resources.
Metaflow captures the exact software environment for every step and recreates it in remote containers.
An extensible framework for generating HTML-based visual reports (plots, tables, images) attached to specific steps.
Native support for fan-out execution patterns, allowing thousands of parallel tasks to run across a cluster.
Built-in @retry and @catch decorators to handle transient infrastructure failures and edge-case data errors.
A Python API to query metadata, logs, and artifacts from any historical run programmatically.
Running hundreds of model variants manually is error-prone and slow.
Registry Updated:2/7/2026
Visualize the best model using Metaflow Cards.
Managing large language model weights and complex dependencies across steps.
Ensuring data consistency and lineage for recurring business reports.