Cloudera

FEBRUARY 16, 2022

Each project consists of a declarative series of steps or operations that define the data science workflow. We can think of model lineage as the specific combination of data and transformations on that data that create a model. Each user associated with a project performs work via a session. Figure 03: lineage.yaml.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

O'Reilly on Data

JULY 28, 2020

There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ethics and Data Science is a short book that helps developers think through data problems, and includes a checklist that team members should revisit throughout the process. Conclusion.

Marketing 362

Marketing Experimentation Metrics Testing 362

Peter James Thomas

AUGUST 29, 2018

Additionally I have a direct set of reports who drive the standard solutions around tooling, governance, quality, data protection , Data Ethics , Metadata and data glossary and models. Helping organisations become “data-centric” is a key part of what you do.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

Alation

JULY 7, 2022

In 2013 I joined American Family Insurance as a metadata analyst. I was changing careers and had just completed a degree in Library and Information Science. I had always been fascinated by how people find, organize, and access information, so a metadata management role after school was a natural choice.

Metadata 52

Metadata Data-driven Insurance Statistics 52

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 68

Data Strategy Strategy Data Warehouse Prescriptive Analytics 68

Cloudera

AUGUST 30, 2022

A data mesh supports distributed, domain-specific data consumers and views data as a product, with each domain handling its own data pipelines. Towards Data Science ). Solutions that support MDAs are purpose-built for data collection, processing, and sharing.

Data Architecture 77

Data Architecture Data Lake Metadata Data Warehouse 77

AWS Big Data

FEBRUARY 1, 2024

The Common Crawl corpus contains petabytes of data, regularly collected since 2008, and contains raw webpage data, metadata extracts, and text extracts. In addition to determining which dataset should be used, cleansing and processing the data to the fine-tuning’s specific need is required.

Modeling 96

Modeling Metadata Data Processing Unstructured Data 96

Domino Data Lab

JANUARY 21, 2021

This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

IBM Big Data Hub

FEBRUARY 8, 2023

The IBM AI Governance solution automates across the AI lifecycle from data collection, model building, deploying and monitoring. This comprehensive solution comes without the excessive costs of switching from your current data science platform. Model facts are centralized for AI transparency and explainability.

Risk Management 52

Risk Management Risk Modeling Metadata 52

Alation

JANUARY 17, 2023

Data would be pulled from various sources, organized into, say, a table, and loaded into a data warehouse for mass consumption. This was not only time-consuming, but the growing popularity of cloud data warehouses compelled people to rethink this process. Better Data Culture. Good data warehouses should be reliable.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

Cloudera

JANUARY 21, 2021

With CDW, as an integrated service of CDP, your line of business gets immediate resources needed for faster application launches and expedited data access, all while protecting the company’s multi-year investment in centralized data management, security, and governance. Cost-optimization and ease-of-use .

Data Lake 93

Data Lake Data Warehouse IT Analytics 93

CIO Business Intelligence

SEPTEMBER 22, 2023

Others aim simply to manage the collection and integration of data, leaving the analysis and presentation work to other tools that specialize in data science and statistics. DMP vs. CDP Lately a cousin of DMP has evolved, called the customer data platform (CDP).

Management 105

Management Advertising Data Lake Sales 105

Domino Data Lab

OCTOBER 9, 2019

We’ll examine National Oceanic and Atmospheric Administration (NOAA) data management practices which I learned about at their workshop, as a case study in how to handle data collection, dataset stewardship, quality control, analytics, and accountability when the stakes are especially high. Data Science meets Climate Science.

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

AWS Big Data

FEBRUARY 21, 2023

The data vault approach is a method and architectural framework for providing a business with data analytics services to support business intelligence, data warehousing, analytics, and data science needs. Amazon Redshift RA3 instances and Amazon Redshift Serverless are perfect choices for a data vault.

Data Warehouse 65

Data Warehouse Data Lake Cost-Benefit Modeling 65

O'Reilly on Data

MARCH 18, 2020

In 2019, 57% of respondents cited a lack of ML modeling and data science expertise as an impediment to ML adoption; this year, slightly more—close to 58%—did so. The bad news is that AI adopters—much like organizations everywhere—seem to treat data governance as an additive rather than an essential ingredient.

Enterprise 309

Enterprise Deep Learning Data Governance Risk 309

CIO Business Intelligence

JUNE 9, 2022

Others aim simply to manage the collection and integration of data, leaving the analysis and presentation work to other tools that specialize in data science and statistics. Lately a cousin of DMP has evolved, called the customer data platform (CDP). Adobe Audience Manager. OnAudience.

Management 144

Management Advertising Data Lake Sales 144

Andrew White

JANUARY 9, 2022

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. We write about data and analytics.

Analytics 64

Analytics Measurement Modeling Data-driven 64

The Unofficial Google Data Science Blog

SEPTEMBER 9, 2015

Record-level program scope As a data scientist, you write a Sawzall script to operate at the level of a single record. The scope of each record is determined by the source of the data; it might be a web page, metadata about an app, or logs from a web server. However, it turns out to be quite useful for data science applications.

Data Science 56

Data Science Statistics Testing Metadata 56

Rocket-Powered Data Science

MARCH 20, 2023

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Datasphere is not just for data managers.

Data Warehouse 324

Data Warehouse Metadata Digital Transformation Unstructured Data 324

Alation

DECEMBER 15, 2021

Modern business is built on a foundation of trusted data. Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

datapine

JANUARY 31, 2022

Although the oil company has been producing massive amounts of data for a long time, with the rise of new cloud-based technologies and data becoming more and more relevant in business contexts, they needed a way to manage their information at an enterprise level and keep up with the new skills in the data industry.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 261

Machine Learning Software Modeling Testing 261

Alation

FEBRUARY 17, 2023

The firms that get data governance and management “right” bring people together and leverage a set of capabilities: (1) Agile; (2) Six sigma; (3) data science; and (4) project management tools. So, establishing a framework to store data by its source is a great place to start. Here’s an example.

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

Andrew White

MARCH 31, 2023

See Product Management Practices Crucial for Data and Analytics Asset Monetization. Data mesh versus data fabric I am not the expert here but in lay terms, I believe both fabric and mesh include a semantic inference engine that consumes active metadata. Both build semantic maps that span silos of data.

Analytics 40

Analytics Marketing Visualization Data-driven 40