ODSC - Open Data Science

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

ETL 59

ETL Data Pipeline ML ML 59

Alation

AUGUST 26, 2021

Under an active data governance framework , a Behavioral Analysis Engine will use AI, ML and DI to crawl all data and metadata, spot patterns, and implement solutions. Data Governance and Data Strategy. Finally, data catalogs leverage behavioral metadata to glean insights into how humans interact with data.

Data Governance 64

Data Governance ML ML Augmented Analytics 64

Alation

MAY 24, 2022

Alation has been leading the evolution of the data catalog to a platform for data intelligence. Higher data intelligence drives higher confidence in everything related to analytics and AI/ML. It will allow for layout customization and better version history tracking to determine how it has changed over time.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

Alation

SEPTEMBER 7, 2021

TrustCheck can be integrated with popular business intelligence BI tools, like Tableau, which supply quality information as you use these tools. In addition, Alation provides a quick preview and sample of the data to help data scientists and analysts with greater data quality insights.

Data Governance 59

Data Governance Data Scientist Data Quality Data Profiling 59

DagsHub

AUGUST 23, 2024

As companies increasingly rely on data for decision-making, poor-quality data can lead to disastrous outcomes. Even the most sophisticated ML models, neural networks, or large language models require high-quality data to learn meaningful patterns. When bad data is inputted, it inevitably leads to poor outcomes.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52