Alation

SEPTEMBER 7, 2021

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. But governance is a time-consuming process (for users and data stewards alike).

Data Governance 59

Data Governance Data Scientist Data Quality Data Profiling 59

Heartbeat

JUNE 12, 2023

The primary goal of model monitoring is to ensure that the model remains effective and reliable in making predictions or decisions, even as the data or environment in which it operates evolves. This monitoring requires robust data management and processing infrastructure. We pay our contributors, and we don’t sell ads.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data. What is Data Observability?

Data Observability 52

Data Observability Data Quality Data Pipeline Data Governance 52

Dataconomy

JULY 28, 2023

Data preprocessing ensures the removal of incorrect, incomplete, and inaccurate data from datasets, leading to the creation of accurate and useful datasets for analysis ( Image Credit ) Data completeness One of the primary requirements for data preprocessing is ensuring that the dataset is complete, with minimal missing values.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Snorkel AI

OCTOBER 4, 2023

FMs can even transform dense tabular data into digestible consumer profiles. Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex data profiles on potential borrowers.

AI 64

AI AI ML ML 64

Snorkel AI

OCTOBER 4, 2023

FMs can even transform dense tabular data into digestible consumer profiles. Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex data profiles on potential borrowers.

AI 59

AI AI ML ML 59

Snorkel AI

OCTOBER 4, 2023

FMs can even transform dense tabular data into digestible consumer profiles. Data scientists can train large language models (LLMs) and generative AI like GPT-3.5 to generate natural language reports from tabular data that help human agents easily interpret complex data profiles on potential borrowers.

AI 52

AI AI ML ML 52

The MLOps Blog

MAY 17, 2023

ETL pipeline | Source: Author These activities involve extracting data from one system, transforming it, and then processing it into another target system where it can be stored and managed. ML heavily relies on ETL pipelines as the accuracy and effectiveness of a model are directly impacted by the quality of the training data.

ETL 59

ETL Data Pipeline ML ML 59

Snorkel AI

MAY 12, 2023

My name is Erin Babinski and I’m a data scientist at Capital One, and I’m speaking today with my colleagues Bayan and Kishore. We’re here to talk to you all about data-centric AI. All right, so let’s set the stage first with some examples: a focus on data quality leads to better ML-powered products.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

My name is Erin Babinski and I’m a data scientist at Capital One, and I’m speaking today with my colleagues Bayan and Kishore. We’re here to talk to you all about data-centric AI. All right, so let’s set the stage first with some examples: a focus on data quality leads to better ML-powered products.

Machine Learning 52

Machine Learning Machine Learning ML ML 52