The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

Natural Language Processing Best Practices & Examples

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

股票AI操盘手：从学习、模拟到实盘，一站式平台。包含股票知识、策略实例、大模型、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等，可以方便学习、模拟及实盘交易

Koalas: pandas API on Apache Spark

A comprehensive Python package template to kickstart and standardize your MLOps initiatives and data pipelines.

This is the github repo for Learning Spark: Lightning-Fast Data Analytics [2nd Edition]

No description available

An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more

DoEKS is a tool to build, deploy and scale Data Platforms on Amazon EKS

Books / PDFS / EPUBS for different fields of programming . READ GROW AND ENJOY 😊😊😊😊

deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow†, and MLflow† into a complete platform.

🍫 Example code for a basic ML Platform based on Pulumi, FastAPI, DVC, MLFlow and more

Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...

Framework to bring LLM applications to production

All the available resources to master MLOPS from scratch

ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python

Crane is a easy-to-use and beautiful desktop application helps you build manage your container images.

An example MLflow project

Ready to run docker-compose configuration for ML Flow with Mysql and Minio S3

Flexible and scalable template based on PyTorch Lightning + Hydra. Efficient workflow and reproducibility for rapid ML experiments.

Partly lecture and partly a hands-on tutorial and workshop, this is a three part series on how to get started with MLflow. In this three part series, we will cover MLflow Tracking, Projects, Models, and Model Registry.

aim-mlflow integration

Basic and advanced MLflow examples for many ML flavors

Repository with code examples of mlflow

A kedro-plugin for integration of mlflow capabilities inside kedro projects (especially machine learning model versioning and packaging)

PipelineX: Python package to build ML pipelines for experimentation with Kedro, MLflow, and more

No description available

MLflow deployment with 1 command

📁 This repository hosts a growing collection of AI blueprint projects that run end-to-end using Jupyter notebooks, MLflow deployments, and Streamlit web apps.🛠️ All projects are built using HP AI Studio with ❤️ If you find this useful, please don’t forget to star the repository ⭐ and support our work 🚀