The Best Talos Alternatives in 2026

Machine learning operations and large language model operations are the focus of Starwhale’s platform.

Future AGI evaluates and observes AI and agent applications with tracing, simulations, datasets, and guardrails.

This repository contains machine learning code examples that use Comet.ml.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

Build neural networks and support machine learning research with Sonnet, a library based on TensorFlow.

The tensorflow-upstream repository provides a ROCm port of TensorFlow for the project.

Tensorforce provides a TensorFlow library for applied reinforcement learning tasks.

einops provides readable tensor operations for PyTorch, JAX, TensorFlow, and other frameworks.

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.

ModelDB tracks machine learning model versions, metadata, and experiments.

Kedro-Viz visualizes Kedro data and machine learning pipelines and tracks experiments.

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

tfdeploy evaluates TensorFlow graphs and exports them to run with NumPy in environments without TensorFlow.

Helicone provides open-source observability for LLM applications, with tools to monitor, evaluate, and experiment.

DJL is a Java framework for running deep learning models across different engines.

Stakpak runs an agent on a machine to ship code and keep applications running.

TensorFlow offers an open-source machine learning platform with tools, libraries, and community resources.

GeneticSharp provides C# tools for developing applications that use genetic algorithms.

Xinference runs language, speech, and multimodal models through a unified inference API across cloud, on premises, or laptop setups.

OptiMate is a collection of libraries for optimizing AI model performance; the repository is marked as a legacy project.

Mirascope describes itself as an alternative approach to building applications with large language models.

Flower provides a federated AI framework for machine learning projects.

PAI schedules resources and manages clusters for AI workloads across distributed computing environments.

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.