DZone
FEBRUARY 3, 2022
This blog will demonstrate how to set up and benchmark the end-to-end performance of the training process. Architecture. The typical process of using Alluxio to accelerate machine learning and deep learning training includes the following three steps:
Dynatrace
JANUARY 31, 2022
Key takeaways from this article on modern observability for serverless architecture: As digital transformation accelerates, organizations need to innovate faster and continually deliver value to customers. Companies often turn to serverless architecture to accelerate modernization efforts while simplifying IT management.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Uber Engineering
JULY 7, 2021
Since we productionized distributed XGBoost on Apache Spark™ at Uber in 2017, XGBoost has powered a wide spectrum of machine learning (ML) use cases at Uber, spanning from optimizing marketplace dynamic pricing policies for Freight , improving times of … The post Elastic Distributed Training with XGBoost on Ray appeared first on Uber Engineering (..)
The Netflix TechBlog
NOVEMBER 13, 2023
Practical use cases for speech & music activity Audio dataset preparation Speech & music activity is an important preprocessing step to prepare corpora for training. Content, genre and languages Instead of augmenting or synthesizing training data, we sample the large scale data available in the Netflix catalog with noisy labels.
Dynatrace
JANUARY 31, 2024
Retrieval-augmented generation emerges as the standard architecture for LLM-based applications Given that LLMs can generate factually incorrect or nonsensical responses, retrieval-augmented generation (RAG) has emerged as an industry standard for building GenAI applications. This is equivalent to driving 123 gas-powered cars for a whole year.
Dynatrace
JANUARY 4, 2024
While this approach can be effective if the model is trained with a large amount of data, even in the best-case scenarios, it amounts to an informed guess, rather than a certainty. Because IT systems change often, AI models trained only on historical data struggle to diagnose novel events. That’s where causal AI can help.
Uber Engineering
DECEMBER 10, 2019
Michelangelo , Uber’s machine learning (ML) platform, powers machine learning model training across various use cases at Uber, such as forecasting rider demand , fraud detection , food discovery and recommendation for Uber Eats , and improving the accuracy of … The post Productionizing Distributed XGBoost to Train Deep Tree Models with Large (..)
Expert insights. Personalized for you.
246 
Let's personalize your content