Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Automated workflows for data product creation, testing and deployment.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

Octopai

MAY 2, 2022

Data analytics ain’t what it used to be. As a data analyst, you’re no longer just providing data analytics services. You’re providing data analytics products. . Today, your business users have the same perspective on data analytics. Enter DataOps. What is DataOps? Issue detected?

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

O'Reilly on Data

MARCH 31, 2020

This means that the AI products you build align with your existing business plans and strategies (or that your products are driving change in those plans and strategies), that they are delivering value to the business, and that they are delivered on time. AI product estimation strategies.

Management 332

Management Machine Learning Experimentation Metrics 332

Alation

SEPTEMBER 28, 2022

The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In order to make DataOps successful in an organization, there needs to be a shift in culture and mindset around how data is managed.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 307

Testing Machine Learning Consulting Data Quality 307

IBM Big Data Hub

JANUARY 17, 2023

Due to the convergence of events in the data analytics and AI landscape, many organizations are at an inflection point. From there, it can be easily accessed via dashboards by data consumers or those building into a data product. Data science and MLOps. AI is no longer experimental. Start a trial.

Data-driven 57

Data-driven Enterprise Data Governance Prescriptive Analytics 57

AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

Data Lake 88

Data Lake Analytics Snapshot Optimization 88