AWS Machine Learning - AI
MARCH 28, 2024
Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl , C4 , Wikipedia, and ArXiv. The resulting LLM outperforms LLMs trained on non-domain-specific datasets when tested on finance-specific tasks.
Netflix Tech
NOVEMBER 11, 2022
Data analysis and machine learning techniques are great candidates to help secure large-scale streaming platforms. In semi-supervised anomaly detection models, only a set of benign examples are required for training. That’s up to the machine learning model to discover and avoid such false-positive incidents.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Perficient
FEBRUARY 27, 2023
Machine learning is now being used all around the world and its helping analytics team greatly in saving costs and improving business decisions. A Machine learning project starts with Raw data and Ends with a web application that can predict outcomes and generate insights from raw data.
O'Reilly Media - Data
DECEMBER 12, 2019
For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 8] Data about individuals can be decoded from ML models long after they’ve trained on that data (through what’s known as inversion or extraction attacks, for example). ML security audits.
O'Reilly Media - Data
JULY 17, 2018
Although machine learning (ML) can produce fantastic results, using it in practice is complex. For example, Uber and Facebook have built Michelangelo and FBLearner Flow to manage data preparation, model training, and deployment. Machine learning workflow challenges. algorithm) to see whether it improves results.
Altexsoft
JULY 23, 2020
When speaking of machine learning, we typically discuss data preparation or model building. The fusion of terms “machine learning” and “operations”, MLOps is a set of methods to automate the lifecycle of machine learning algorithms in production — from initial model training to deployment to retraining against new data.
O'Reilly Media - Ideas
JUNE 9, 2020
The biggest problem facing machine learning today isn’t the need for better algorithms; it isn’t the need for more computing power to train models; it isn’t even the need for more skilled practitioners. It’s getting machine learning from the researcher’s laptop to production.
Expert insights. Personalized for you.
94 
Let's personalize your content