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MLflow

Appears in 6 awesome lists

is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components:

Open mlflow.org

Found in these lists

AWESOME DATA SCIENCE

Section: Miscellaneous Tools · MLOps framework for managing ML models across their full lifecycle

FreshScore 92

Awesome Machine Learning

Section: Tools · platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.

FreshScore 93

Awesome MLOps

Section: Model Lifecycle · Open source platform for the machine learning lifecycle.

FreshScore 80

Awesome Software Engineering for Machine Learning

Section: Tooling · Manage the ML lifecycle, including experimentation, deployment, and a central model registry.

StaleScore 51

Table of Contents

Section: ML frameworks & applications · is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components:

SlowScore 61

Awesome Generative AI

Section: Developer tools · An open-source platform for tracking ML experiments, evaluating models and prompts, deploying models, and adding LLM observability. #opensource

FreshScore 90

m2cgen

Transpile trained ML models into other languages. sklearn-porter - Transpile trained scikit-learn estimators to C, Java, JavaScript and others. mlflow - Manage the machine learning lifecycle, including experimentation, reproducibility and deployment. skll - Command-line utilities to make it easier…

In 16 listsDetails

Opik

Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…

In 16 listsDetails

Apache Airflow

"Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it…

In 13 listsDetails

Ray

A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. ray.io

In 13 listsDetails

Gradio

Build and share delightful machine learning apps, all in Python. The de facto standard for creating interactive ML demos with automatic UI generation from function signatures. Powers thousands of Hugging Face Spaces.

In 11 listsDetails

Prefect

Workflow management system that makes it easy to take your data pipelines and add semantics like retries, logging, dynamic mapping, caching, failure notifications, and more.

In 11 listsDetails

MindsDB

MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.

In 11 listsDetails

promptfoo

Test your prompts, models, RAGs. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. LLM evals for OpenAI/Azure GPT, Anthropic Claude, VertexAI Gemini, Ollama, Local & private models like Mistral/Mixtral/Llama with CI/CD

In 11 listsDetails