ODSC - Open Data Science

OCTOBER 25, 2024

Delphina Demo: AI-powered Data Scientist Jeremy Hermann | Co-founder at Delphina | Delphina.Ai In this demo, you’ll see how Delphina’s AI-powered “junior” data scientist can transform the data science workflow, automating labor-intensive tasks like data discovery, transformation, and model building.

Data Scientist 52

Data Scientist Software Engineer Automation AI 52

Pickl AI

OCTOBER 14, 2024

Clustering: Grouping similar data points to identify segments within the data. Applications EDA is widely employed in research and data discovery across industries. Researchers use EDA to better understand their data before conducting more formal statistical analyses.

Data Analysis 52

Data Analysis Data Mining Data Discovery Machine Learning 52

DataRobot Blog

APRIL 1, 2018

In Rita Sallam’s July 27 research, Augmented Analytics , she writes that “the rise of self-service visual-bases data discovery stimulated the first wave of transition from centrally provisioned traditional BI to decentralized data discovery.” We agree with that.

Data Discovery 40

Data Discovery Data Integration Machine Learning Data Quality 40

The MLOps Blog

MAY 17, 2023

Here are some specific reasons why they are important: Data Integration: Organizations can integrate data from various sources using ETL pipelines. This provides data scientists with a unified view of the data and helps them decide how the model should be trained, values for hyperparameters, etc.

ETL 59

ETL ML Machine Learning Data Scientist 59

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52