40 Open source projects that are alternatives of or similar to ForestFlow

Examples showcasing model deployment

Statistical Learning & Data Mining IV - H2O Presenation & Tutorial

'Deploying machine learning models with a Flask API' tutorial, written for HyperionDev

DEPRECATED Build, manage and deploy H2O's high-speed machine learning models.

🤖 A machine learning framework for audio, text, image, video, or .CSV files (50+ featurizers and 15+ model trainers).

Python binding for the H2O HTTP server

Serving TensorFlow models with TensorFlow Serving 📙

Simple and Distributed Machine Learning

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

Mercury-ML is an open source Machine Learning workflow management library. Its core contributors are employees of Alexander Thamm GmbH

BentoML Example Projects 🎨

NOTE: skutil is now deprecated. See its sister project: https://github.com/tgsmith61591/skoot. Original description: A set of scikit-learn and h2o extension classes (as well as caret classes for python). See more here: https://tgsmith61591.github.io/skutil

Exemplary, annotated machine learning pipeline for any tabular data problem.

Identifying diseases in chest X-rays using convolutional neural networks

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

Serving PyTorch models with TorchServe 🔥

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

CRAN Task View: Model Deployment with R

Materials for GWU DNSC 6279 and DNSC 6290.

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Comparison tools

Tutorials and training material for the H2O Machine Learning Platform

RSparkling: Use H2O Sparkling Water from R (Spark + R + Machine Learning)

Deep Learning UDF for KSQL, the Streaming SQL Engine for Apache Kafka with Elasticsearch Sink Example

docker files for h2o http2 webserver

Verteego Data Suite

Sparkling Water provides H2O functionality inside Spark cluster

A curated list of gradient boosting research papers with implementations.

This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

Presentations from H2O meetups & conferences by the H2O.ai team

cuDF - GPU DataFrame Library

Access log profiler based on response time

A curated list of research, applications and projects built using the H2O Machine Learning platform

Simple and Distributed Machine Learning

Model Serving Made Easy

Time Series Forecasting Best Practices & Examples