H2O.ai Blog
By: H2O.ai
Math Algorithms have primarily been the domain of desktop data science. With the success of scalable algorithms at Google, Amazon, and Netflix, there is an ever growing demand for sophisticated algorithms over big data. In this talk, we get a ringside view in the making of the world's most scalable and fastest machine learning framework, H2O, and the performance lessons learnt scaling it over EC2 for Netflix and over commodity hardware for other power users.
Top 10 Performance Gotchas is about the white hot stories of i/o wars, S3 resets, and muxers, as well as the power of primitive byte arrays, non-blocking structures, and fork/join queues. Of good data distribution & fine-grain decomposition of Algorithms to fine-grain blocks of parallel computation. It's a 10-point story of the rage of a network of machines against the tyranny of Amdahl while keeping the statistical properties of the data and accuracy of the algorithm.
Update
Slides from the talk
H2O LLM DataStudio Part II: Convert Documents to QA Pairs for fine tuning of LLMs
Convert unstructured datasets to Question-answer pairs required for LLM fine-tuning and other downstream tasks with
September 22, 2023 - by Genevieve Richards, Tarique Hussain and Shivam Bansal
Building a Fraud Detection Model with H2O AI Cloud
In a previous article[1], we discussed how machine learning could be harnessed to mitigate fraud.
July 28, 2023 - by Asghar Ghorbani
A Look at the UniformRobust Method for Histogram Type
Tree-based algorithms, especially Gradient Boosting Machines (GBM's), are one of the most popular algorithms used.
July 25, 2023 - by Hannah Tillman and Megan Kurka
H2O LLM EvalGPT: A Comprehensive Tool for Evaluating Large Language Models
In an era where Large Language Models (LLMs) are rapidly gaining traction for diverse applications,
July 19, 2023 - by Srinivas Neppalli, Abhay Singhal and Michal Malohlava
