AI Services Catalog

A hyperparameter optimization framework, inspired by Optuna.

Fast and lightweight AutoML (paper).

An open source python library for AutoML.

a basic proof of concept for genetic architecture search in Keras.

An open source python library for scalable Bayesian optimisation.

an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

Automatic architecture search and hyperparameter optimization for PyTorch.

AutoML library for deep learning.

Provide an input CSV and a target field to predict, generate a model + code to run it.

An autoML framework & toolkit for machine learning on graphs

A framework to find the best performing AI/ML model for any AI problem.

a platform for Neural Network Search (NAS) that allows you to generate efficient deep networks for your applications.

a high-performance, high-precision CPU, GPU, and memory profiler for Python

octoml-profile is a python library and cloud service designed to provide the simplest experience for assessing and optimizing the performance of PyTorch models on cloud hardware with state-of-the-art ML acceleration technology.

Open deep learning compiler stack for cpu, gpu and specialized accelerators

Accessible large language models via k-bit quantization for PyTorch.

Compiler technology to transform a valid Open Neural Network Exchange (ONNX) graph into code that implements the graph with minimum runtime support.

Docker for Your ML/DL Models Based on OCI Artifacts

Open Source ML Model Versioning, Metadata, and Experiment Management

Light-weight, universal resource scheduler for container orchestrator systems.

A Cloud Native Batch System (Project under CNCF).

A Highly Scalable Workload Manager.

Kubernetes-native Job Queueing.

Enterprise-grade Voice AI simulation SDK for scenario-driven stress testing of multimodal and agentic systems.

An open-source unstructured data ETL tool to streamline the end-to-end unstructured data processing pipeline.

The easiest way to automate your data.

The fastest way to build data pipelines. Develop iteratively, deploy anywhere.

Build and manage real-life data science projects with ease!

Machine Learning Pipelines for Kubeflow.

Durable execution layer for AI agents. Checkpoints, replay, resume, and observability primitives that make agent workflows persistent and replayable — no graph DSL required.

Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale.

Workflow engine for Kubernetes.

An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

One-click machine learning deployment (LLM, text-to-image and so on) at scale on any cluster (GCP, AWS, Lambda labs, your home lab, or even a single machine).

An effortless infrastructure for machine learning built on the top of Kubernetes.

Resource scheduling and cluster management for AI.

Machine Learning Toolkit for Kubernetes.

Standardized Serverless ML Inference Platform on Kubernetes

ModelFox is a platform for managing and deploying machine learning models.

Open source platform for the machine learning lifecycle.

??️ distilabel is a framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.

An open source feature store for machine learning.

The Virtual Feature Store. Turn your existing data infrastructure into a feature store.

A Python library that facilitates fast and easy data exploration by automating the visualization and data analysis process.

A CLI tool that allows you to build data profiles and write assertion tests for easily evaluating and tracking your data's reliability over time.

Modern columnar data format for ML implemented in Rust.

Git-like capabilities for your object storage.