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Different Jobs Related to the Data Industry

#artificialintelligence

Data Analyst: A Data Analyst interprets data and turns it into information which can offer ways to improve a business, thus affecting business decisions. Data Analysts gather information from various sources and interpret patterns and trends -- as such a Data Analyst job description should highlight the analytical nature of the role. Once data has been gathered and interpreted, the Data Analyst will report back what has been found in a comprehensive study to the wider business/relevant colleagues. "Successful data analysts understand how to create narratives with data," says Jess Kendra, manager of analytics at Porter Novelli. "To remain valuable, the reports, answers, and insights that data analysis provides have to be understood by the next decision-maker, who frequently is not an analyst."


The VLS Tech-Assist Expert System

AI Magazine

The vertical launch system (vls) tech-assist expert system is being used by the in-service engineering agent as a force multiplier to maintain the readiness, with fewer resources, of a growing population of vlss in the U.S. Navy fleet. This article describes the collaborative development of this knowledge-based system for diagnosis; its main features, including case-based and model-based reasoning; and the lessons we learned from the process.


The Road Ahead for Knowledge Management: An AI Perspective

AI Magazine

Enabling organizations to capture, share, and apply the collective experience and know-how of their people is seen as fundamental to competing in the knowledge economy. As a result, there has been a wave of enthusiasm and activity centered on knowledge management. To make progress in this area, issues of technology, process, people, and content must be addressed. In this article, we develop a road map for knowledge management. It begins with an assessment of the current state of the practice, using examples drawn from our experience at Schlumberger. It then sketches the possible evolution of technology and practice over a 10-year period. Along the way, we highlight ways in which AI technology, present and future, can be applied in knowledge management systems.


The Road Ahead for Knowledge Management

AI Magazine

Enabling organizations to capture, share, and apply the collective experience and know-how of their people is seen as fundamental to competing in the knowledge economy. As a result, there has been a wave of enthusiasm and activity centered on knowledge management. To make progress in this area, issues of technology, process, people, and content must be addressed. In this article, we develop a road map for knowledge management. It begins with an assessment of the current state of the practice, using examples drawn from our experience at Schlumberger.


DeepTriage: Automated Transfer Assistance for Incidents in Cloud Services

arXiv.org Artificial Intelligence

As cloud services are growing and generating high revenues, the cost of downtime in these services is becoming significantly expensive. To reduce loss and service downtime, a critical primary step is to execute incident triage, the process of assigning a service incident to the correct responsible team, in a timely manner. An incorrect assignment risks additional incident reroutings and increases its time to mitigate by 10x. However, automated incident triage in large cloud services faces many challenges: (1) a highly imbalanced incident distribution from a large number of teams, (2) wide variety in formats of input data or data sources, (3) scaling to meet production-grade requirements, and (4) gaining engineers' trust in using machine learning recommendations. To address these challenges, we introduce DeepTriage, an intelligent incident transfer service combining multiple machine learning techniques - gradient boosted classifiers, clustering methods, and deep neural networks - in an ensemble to recommend the responsible team to triage an incident. Experimental results on real incidents in Microsoft Azure show that our service achieves 82.9% F1 score. For highly impacted incidents, DeepTriage achieves F1 score from 76.3% - 91.3%. We have applied best practices and state-of-the-art frameworks to scale DeepTriage to handle incident routing for all cloud services. DeepTriage has been deployed in Azure since October 2017 and is used by thousands of teams daily.