O'Reilly on Data

SEPTEMBER 12, 2018

Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more. Below you'll find links to highlights from the event. Preserving privacy and security in machine learning. Watch " Managing risk in machine learning.". The future of data warehousing.

IoT 144

IoT Machine Learning Deep Learning Data Science 144

IBM Big Data Hub

APRIL 22, 2024

Energy transition and climate resilience Applying AI and IoT to accelerate the transition to sustainable energy sources There is a clear need (link resides ibm.com) to accelerate the transition to low-carbon energy sources and transform infrastructures to build more climate-resilient organizations.

IoT 77

IoT Internet of Things Cost-Benefit Reporting 77

CIO Business Intelligence

JUNE 2, 2022

These technologies include deep learning , AI-powered robotic process automation , augmented reality , data mesh (a distributed architecture for data management), blockchain or distributed ledger technology, low-code platforms, progressive web apps , service mesh and event-driven architectures.

Predictive Analytics 138

Predictive Analytics Technology Analytics Machine Learning 138

AWS Big Data

MAY 16, 2024

In our increasingly interconnected world, where every step we take, every location we visit, and every event we encounter leaves a digital footprint, the volume and complexity of geospatial data are expanding at an astonishing pace. This insight aids in predicting future demand and facilitates operations-related decisions.

Data Warehouse 108

Data Warehouse Visualization Cost-Benefit Optimization 108

O'Reilly on Data

MARCH 24, 2020

John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing. This need will grow as smart devices, IoT, voice assistants, drones, and augmented and virtual reality become more prevalent.

Machine Learning 352

Machine Learning Statistics Data Quality Modeling 352

Cloudera

SEPTEMBER 11, 2020

A critical call to action here is that retail and consumer goods companies must incorporate a broader set of ‘demand signals’ than what they may have at hand, including external data sets like weather, geopolitical implications, local event calendars, demand transfer, and healthcare data. . Supply-side. Demand-side. Automation opportunities.

Manufacturing 92

Manufacturing Forecasting Digital Transformation Sales 92

Domino Data Lab

MARCH 3, 2019

O’Reilly Media had an earlier survey about deep learning tools which showed the top three frameworks to be TensorFlow (61% of all respondents), Keras (25%), and PyTorch (20%)—and note that Keras in this case is likely used as an abstraction layer atop TensorFlow. The data types used in deep learning are interesting.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49