Sample AI Apps built with H2O Wave.
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Updated
Apr 17, 2024 - Python
Sample AI Apps built with H2O Wave.
autoEnsemble : An AutoML Algorithm for Building Homogeneous and Heterogeneous Stacked Ensemble Models by Searching for Diverse Base-Learners
An AI-powered application leveraging machine learning for detecting fraudulent activities in financial transactions with MLOps.
Production ready templates for deploying Driverless AI (DAI) scorers. https://h2oai.github.io/dai-deployment-templates/
The Movie Recommendation System is an application designed to suggest movies to users based on their preferences and viewing history. It employs collaborative filtering techniques to analyze user behavior and generate personalized recommendations.
My solutions for the 2023 Cloudflight Coding Contest (AI Category)
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.
Docker Locker for common Development Containers including Jenkins, Supabase, Portainer, Grafana & Rancher
Deep Learning UDF for KSQL for Streaming Anomaly Detection of MQTT IoT Sensor Data
Diverse collection of 100 Hydrogen Torch Use-Cases by different industries, data-types, and problem types
The project aims to predict if a customer's license should be issued, renewed, or cancelled depending on various parameters in business license dataset
A curated list of research, applications and projects built using the H2O Machine Learning platform
R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
Presentations from H2O meetups & conferences by the H2O.ai team
An example of how to provide a CD pipeline enabled h2o Machine Learning project Edit
Files for compiling my presentation about H2O.ai.
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...
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