Dataconomy

MARCH 4, 2025

The data mining process The data mining process is structured into four primary stages: data gathering, data preparation, data mining, and data analysis and interpretation. Each stage is crucial for deriving meaningful insights from data.

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 91

DagsHub

FEBRUARY 29, 2024

Data, is therefore, essential to the quality and performance of machine learning models. This makes data preparation for machine learning all the more critical, so that the models generate reliable and accurate predictions and drive business value for the organization. million per year.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently. For them, the end-to-end MLOps lifecycle and infrastructure is necessary.

AI 133

AI AI ML ML 133

Flipboard

NOVEMBER 24, 2023

Data scientists and ML engineers require capable tooling and sufficient compute for their work. Therefore, BMW established a centralized ML/deep learning infrastructure on premises several years ago and continuously upgraded it.

ML 149

ML ML AWS AI 149

ODSC - Open Data Science

NOVEMBER 19, 2024

Here’s a breakdown of ten top sessions from this year’s conference that data professionals should consider. Topological Deep Learning Made Easy with TopoX with Dr. Mustafa Hajij Slides In these AI slides, Dr. Mustafa Hajij introduced TopoX, a comprehensive Python suite for topological deep learning.

Deep Learning 52

Deep Learning Deep Learning Data Science AI 52

The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Monitor the performance of machine learning models.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

IBM Journey to AI blog

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. It helps facilitate the entire data and AI lifecycle, from data preparation to model development, deployment and monitoring.

AI 110

AI AI Data Warehouse Machine Learning 110