ODSC - Open Data Science

MARCH 22, 2023

A recent report by Cloudfactory found that human annotators have an error rate between 7–80% when labeling data (depending on task difficulty and how much annotators are paid).

ML 88

ML ML Data Scientist AI 88

Towards AI

FEBRUARY 11, 2025

Path to Maturity – in data engineering often looks like this: Junior: Ill fix it with code Mid-level: Ill build a system to prevent it Senior: Lets understand why this happens Lead: We need to change how we work Image by Author The best technical solution cant fix a broken process. Another challenge is data integration and consistency.

Data Quality 54

Data Quality Data Engineer Data Engineering Data Engineering 54

How to Learn Machine Learning

OCTOBER 10, 2024

Data Cleaning To ensure model success, it’s crucial to clean data thoroughly, eliminating noise, bias, and inaccuracies. Data Labeling Accurate labeling is extremely important in supervised learning.

Machine Learning 52

Machine Learning Machine Learning Data Quality Algorithm 52

IBM Journey to AI blog

AUGUST 15, 2023

Building and training foundation models Creating foundations models starts with clean data. This includes building a process to integrate, cleanse, and catalog the full lifecycle of your AI data. A hybrid multicloud environment offers this, giving you choice and flexibility across your enterprise.

Artificial Intelligence 93

Artificial Intelligence Artificial Intelligence AI AI 93

AWS Machine Learning Blog

NOVEMBER 27, 2023

As AI adoption continues to accelerate, developing efficient mechanisms for digesting and learning from unstructured data becomes even more critical in the future. This could involve better preprocessing tools, semi-supervised learning techniques, and advances in natural language processing. read HTML).

Data Preparation 95

Data Preparation AI AI Python 95

Women in Big Data

DECEMBER 13, 2023

A Large Language Model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabeled text using self-supervised learning or semi-supervised learning.LLM works on the Transformer Architecture.

Natural Language Processing 52

Natural Language Processing Big Data Big Data Computer Science 52

DagsHub

AUGUST 2, 2023

ML engineers need access to a large and diverse data source that accurately represents the real-world scenarios they want the model to handle. Insufficient or poor-quality data can lead to models that underperform or fail to generalize well. Gathering high-quality and sufficient data can be time and effort-consuming.

ML 52

ML ML Data Engineering Data Engineering 52