AI/ML

October 28, 2019

GOVERNMENT

Data Gravity: Driving Design for Intelligent Mission Fabric

What is Data Gravity and what does it mean for artificial intelligence and machine learning (AI/ML) and government networks?

September 23, 2019

DATA CENTER

Cisco Data Intelligence Platform Gets Real

Data scientists constant search for newer techniques and technologies that can unlock the power of their data is leading them towards artificial intelligence (AI) and machine learning (ML) tools and...

September 17, 2019

CUSTOMER EXPERIENCE

11 Rules of Innovation from Cisco CX’s Chief Architect

Discover 11 guiding principles Cisco CX's Chief Architect uses to drive innovation and digital transformation for some of the world’s largest companies.

September 6, 2019

FINANCIAL SERVICES

AI and machine learning are here—is your workforce ready?

Retail banks are running with artificial intelligence and machine-learning technologies. Armed with these tools, they’re adding speed and ease to banking, completely transforming the customer experience.

August 26, 2019

EXECUTIVE PLATFORM

Cisco Intends to Acquire CloudCherry to Enhance Cisco Contact Center Portfolio

CloudCherry will augment Cisco's contact center portfolio with advanced analytics, rich customer journey mapping, and sophisticated survey capabilities in our cloud, hosted, and on-premises solutions. 

August 22, 2019

DEVELOPER

Artificial Intelligence and Machine Learning at the Edge

The opportunities for innovative solutions utilizing Inference at the Edge are great - smart cities, voice/image recognition, predictive maintenance in factories, collision avoidance....

Network Assurance with Machine Reasoning and Machine Learning

The AI technology landscape includes both ML and MR. The two provide complimentary capabilities and combined together they enable AIOps to cover all aspects of network assurance.  ML captures the patterns in the data, and MR captures human expertise, facts and logic.

June 3, 2019

DATA CENTER

Operationalizing AI/ML

Cisco believes that it is critical for IT to be part of the data science team, and that integrated teams can make a business impact with data science.