There are 1 repository under h2oai topic.
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.
Presentations from H2O meetups & conferences by the H2O.ai team
A curated list of research, applications and projects built using the H2O Machine Learning platform
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...
Deep Learning UDF for KSQL for Streaming Anomaly Detection of MQTT IoT Sensor Data
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
Showcase for using H2O and R for scoring for marketing campaign in retail
Contains benchmarking and interpretability experiments on the Adult dataset using several libraries
TSForecasting - Automated Time Series Forecasting Framework
MLflow-tracking server example with Minio and H2O
Shapley Values with H2O AutoML Example (ML Interpretability)
This repository covers h2o ai based implementations
Production ready templates for deploying Driverless AI (DAI) scorers. https://h2oai.github.io/dai-deployment-templates/
Explains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018
Prinz provides ML integrations for Nussknacker.
Diverse collection of 100 Hydrogen Torch Use-Cases by different industries, data-types, and problem types
Terraform module to deploy H2O Driverless AI on Oracle Cloud Infrastructure (OCI)
Machine learning and Deep Learning Hackathon Solutions
Transformation of Akamai Logs with Spark ETL and discover of Values and similarities in logs used SparkML and H2O ML
Repository for Udemy Course: Identify problems with Artificial Intelligence
autoEnsemble : An AutoML Algorithm for Building Homogeneous and Heterogeneous Stacked Ensemble Models by Searching for Diverse Base-Learners
Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.
Statistical regularization
Feature Importance of categorical variables by converting them into dummy variables (One-hot-encoding) can skewed or hard to interpret results. Here I present a method to get around this problem using H2O.
Complete package for all Data Science models using R. Starting form Preprocessing, Data Manipulation, Feature Engineering, Model Building, and Model Validation.
Confluent KSQL - Fork of project to enhance it with a User Defined Function (UDF) for Machine Learning.
Files for compiling my presentation about H2O.ai.
Docker Locker for common Development Containers including Jenkins, Supabase, Portainer, Grafana & Rancher
A H2O Wave App with Spotify data. You can upload your Spotify history data and create a visual analysis for it insantly!