O'Reilly on Data

OCTOBER 19, 2021

It has far-reaching implications as to how such applications should be developed and by whom: ML applications are directly exposed to the constantly changing real world through data, whereas traditional software operates in a simplified, static, abstract world which is directly constructed by the developer. This approach is not novel.

IT 364

IT Testing Experimentation Software 364

Corinium

JUNE 6, 2019

Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

CIO Business Intelligence

MAY 23, 2022

The traditional approach for artificial intelligence (AI) and deep learning projects has been to deploy them in the cloud. For many nascent AI projects in the prototyping and experimentation phase, the cloud works just fine.

Cost-Benefit 98

Cost-Benefit Experimentation Data Lake Deep Learning 98

DataRobot Blog

MARCH 7, 2023

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. Customers can build, run, and manage applications across multiple clouds, on-premises, and at the edge, with the tools of their choice.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

CIO Business Intelligence

MAY 6, 2025

Customer data in Salesforce, product usage data in Snowflake and financials in Oracle none integrated Regional systems using different naming conventions and field formats This fragmentation leads to inconsistent definitions, duplication of work and multiple versions of the truth. Thats a missed opportunity.

Prescriptive Analytics 78

Prescriptive Analytics Predictive Analytics Descriptive Analytics ROI 78