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

DataKitchen

APRIL 13, 2021

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Production Monitoring Only.

Testing 304

Testing Machine Learning Consulting Data Science 304

Domino Data Lab

OCTOBER 14, 2020

Experimentation and collaboration are built into the core of the platform. We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Why Petastorm?

Experimentation 99

Experimentation Deep Learning Modeling Testing 99

Domino Data Lab

JULY 2, 2019

Scale the problem to handle complex data structures. Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Some may ask: “Can’t we all just go back to the glory days of business intelligence, OLAP, and enterprise data warehouses?”

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

CIO Business Intelligence

AUGUST 21, 2024

The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. Adding smarter AI also adds risk, of course.

Risk 136

Risk Insurance Cost-Benefit Software 136

Cloudera

FEBRUARY 16, 2022

This allows data scientists, engineers and data management teams to have the right level of access to effectively perform their role. Given the complexity of some ML models, especially those based on Deep Learning (DL) Convolutional Neural Networks (CNNs), there are limits to interpretability.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

O'Reilly on Data

SEPTEMBER 11, 2019

Deep learning,” for example, fell year over year to No. Increasingly, the term “data engineering” is synonymous with the practice of creating data pipelines, usually by hand. In quite another respect, however, modern data engineering has evolved to support a range of scenarios that simply were not imaginable 40 years ago.

IoT 25

IoT Big Data Data Warehouse Uncertainty 25