Remove Big data Remove Calibration Remove Engineering
article thumbnail

Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

AWS Machine Learning

The data distribution for punt and kickoff are different. Data preprocessing and feature engineering First, the tracking data was filtered for just the data related to punts and kickoff returns. As a baseline, we used the model that won our NFL Big Data Bowl competition on Kaggle.

article thumbnail

What to do with a ‘Watermelon Customer’?

CustomerSuccessBox

Just imagine the enormity of untracked data. This data can uncover the underlying intent of the customer. Moreover, the rule engines are not calibrated frequently and as result the signals are false. Only data and NO insights. CS platforms track each and every data point. Based on rule engines.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

AWS Machine Learning

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data.

article thumbnail

Rule-Based Automation Is Out: Intelligent Automation Is In!

SmartKarrot

AI can learn from its past experiences and formulates predictions based on data. In short, AI is the best decision engine of the intelligent automation platform. Interpret big data. Industries collect mounds and mounds of data in a single day. Like what you are reading? Sign up for our newsletter. contact-form-7].