Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
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Updated
Jun 7, 2024 - Java
Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.
REST web service for the true real-time scoring (<1 ms) of Scikit-Learn, R and Apache Spark models
Pure Java implementation of XGBoost predictor for online prediction tasks.
Tiny Gradient Boosting Tree
Common library for serving TensorFlow, XGBoost and scikit-learn models in production.
Compile your ML Models/ONNX Decision Trees (XGBoost, LightGBM, SciKit-Learn) straight to JVM Bytecode
Display movies in card and recycler view from tmdb website. Features-- 1) shows movie trailers 2) shows all details of movie and their images using glide library 3) Display Top comments and rate movie using reviews from youtube by sentimental analysis in python 4)You can also create your own movie by adding your own actore,buget and run time and…
Real-time IoT network intrusion detection on Kafka — a Spring Boot streaming pipeline with rule-based and XGBoost ML detection, a live dashboard, and an LLM layer (alert enrichment + natural-language chat) that runs free on Groq.
Reactive AI Agent for Fraud Detection
Easily Score & Rank JSON-Encodable Objects with ML
An android application that allow the user to log in (and access to all his data), and connect to external distributors, in order to get the coffee generated by a Machine Learning algorithm
Lightweight java package to make inference using pre-trained xgboost model
Token-based code clone detection combining 16 unsupervised similarity measures with XGBoost. 95.9% F1 on BigCloneBench, 82.2% on Google Code Jam.
Resilient distributed system: Java Spring Boot transaction core + Python FastAPI ML engine + React.js dashboard. Kafka event streaming, Redis caching, PostgreSQL, JWT auth, XGBoost fraud detection — containerized with Docker Compose.
A Java and PostgreSQL application modeling an Airport Purchase Department, featuring Docker containerization for quick database setup.
Java/Spring Boot reference architecture for joint AI-driven ACH payment decisioning. Sub-100µs inference. 220× faster than gradient-boosted baselines.
Pure-JVM XGBoost predictor for low-latency online inference
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