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LFX Insights

ML Model Serving Platforms

Frameworks and platforms designed to deploy, serve, and manage machine learning models in production environments with features for scaling, monitoring, and optimizing inference performance.

10 projects

30,322 contributors

$742M

Kubeflow

Kubeflow is an open source machine learning platform built on Kubernetes that makes deploying and managing ML workflows on Kubernetes simple, portable and scalable. It provides end-to-end orchestration of machine learning pipelines, model training, serving, and experiment tracking.

Contributors

10,327

Organizations

2,277

Software value

$413M

Ray

Ray is an open-source unified framework for scaling AI and Python applications. It provides a simple, universal API for building distributed applications and includes libraries for machine learning, serving, streaming, and more. Ray enables developers to parallelize single-machine code with minimal code changes and scale applications from a laptop to a cluster.

Contributors

8,697

Organizations

1,477

Software value

$49M

ONNX

ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers.

Contributors

8,011

Organizations

988

Software value

$51M

Kserve

The mission of the Project is to develop a highly scalable and standards based model inference platform on Kubernetes for Trusted AI.

Contributors

2,082

Organizations

424

Software value

$217M

Intel XPU Backend for Triton

A backend implementation that enables Triton Inference Server to run inference workloads on Intel XPU hardware accelerators, providing optimized model execution for Intel GPUs and other XPU devices

Contributors

1,034

Organizations

129

Software value

$9.2M

Adlik

Adlik offers a end-to-end optimizing framework for deep learning models whose goal is to accelerate deep learning inference process both on cloud and embedded environments.

Contributors

106

Organizations

7

Software value

$2.7M

Machine Learning eXchange (MLX)

The mission of the Project is to design and implement an open source Data and AI Assets Catalog and Execution Engine that allows the uploading, registration, execution, and deployment of AI pipelines and pipeline components, models, datasets and notebooks.

Contributors

55

Organizations

7

Software value

$376K

ForestFlow

ForestFlow is a scalable policy-based cloud-native machine learning model server. ForestFlow strives to strike a balance between the flexibility it offers data scientists and the adoption of standards while reducing friction between Data Science, Engineering and Operations teams.

Contributors

10

Organizations

7

Software value

$235K

Open AI Accelerator eXchange (OAAX)

This project hasn't been onboarded to LFX Insights.

BentoML

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

This project hasn't been onboarded to LFX Insights.
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