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enterprise architect


Enterprise architects take charge of the digital revolution

ZDNet

Joe McKendrick is an author and independent analyst who tracks the impact of information technology on management and markets. As an independent analyst, he has authored numerous research reports in partnership with Forbes Insights, IDC, and Unisphere Research, a division of Information Today, Inc. Enterprise architects have been adding a new designation to their titles: digital enterprise architect. That's because their roles have been expanding over the past few years, particularly with data analytics being added to their repertoires. That's the word from Thomas Erl, CEO of Arcitura Education, which provides technology skills training to thousands of professionals across the globe, and co-author of A Field Guide to Digital Transformation. "It's a new era for enterprise architects," he says.


Designing the Internet of Things: role for enterprise architects, IoT architects, or both?

ZDNet

We have the still-emerging Internet of Things, a mishmash of dispersed devices, sensors, kiosks, and systems that everyone promises will make our lives such much cooler. Refrigerators will not only talk to grocery stores, but also to their manufacturers to let them know if any performance issues are cropping up. Cars are already starting to talk to their original manufacturers. Aircraft engines are also in direct contact their manufacturers to let them know what's up -- or what may not be going up. Great use cases, but an architectural nightmare that calls for a new role to plan and piece it all together into a coherent and viable system.


AI Architecture : Computer Vision

#artificialintelligence

AI is becoming popular in real life. Many applications are using computer vision by implementing Convolutional Neural network algorithms. Agriculture apps are using CNN based techniques to analyze the crop images for crop's health and viability of seeds. Self driving cars are using them in moving car and other vehicle detection and classification. Video analysis software uses CNN for finding the automobiles, road blocks, and human beings on the road.


The Business Value of RPA (Robotics Process Automation) - News Break

#artificialintelligence

Primary business values of RPA are productivity, efficiency, and customer satisfaction. As part of my business architecture and designer roles, I architected and designed several business solutions using Robotics Process Automation (RPA) tools and processes for large business and government organisations. I also helped the startup companies and sole trading entrepreneurs to add this interesting and emerging technology to their growth agenda. In this post, my focus is on the business value of Robotics Process Automation. I excluded specific tools and service providers.


Business Value and Architectural Overview of R.P.A

#artificialintelligence

In this article, my aim is to briefly introduce what Robotics Process Automation (RPA) is, it's business value, the major use cases, how it works, how we can enhance it, and my experience on architecting solutions to meet the customer requirements. RPA is a rapidly growing technology offering which uses software robots and the capabilities of cognitive computing and artificial intelligence. RPA can be ideal for dealing with the legacy systems, modernising processes, and even can contribute to overall digital transformation goals for many business and government organisations striving to cut cost, increase profitability, and delight consumers. What problems do RPA offerings solve? At the most basic level (level 1), RPA is used to address repetitive and cumbersome work related problems; for example, tedious clerical tasks taking too long to complete and prone to human errors.


4th Annual Global Artificial Intelligence Conference - Webinar - Online Warm-Up (Free)

#artificialintelligence

I will also discuss the common technical challenges of executing A/B tests on ML algorithms, such as infrastructure requirements, connecting online and offline metrics, and handling ramp up periods for online learning algorithms. Overall, the goal of this talk will be to motivate ML practitioners to use A/B testing when evaluating their algorithms and provide them with high-level guidelines on how to do it. Profile Pavel Dmitriev is a Vice President of Data Science at Outreach, where he works on enabling data driven decision making in sales through experimentation and machine learning. He was previously a Principal Data Scientist with Microsoft's Analysis and Experimentation team, where he worked on scaling experimentation in Bing, Skype, and Windows OS. Pavel co-authored numerous papers at top-tier data mining and machine learning conferences, such as WWW, ICSE, KDD, has given keynotes and tutorials at WWW, SIGIR, SEAA, and KDD.


Systems of insight light way ahead for data

@machinelearnbot

Data is only as good as the insights it produces, the actions it influences, and the results it fosters. That is the secret recipe for data management. Big data promises business-changing insights, but technology management is still the largest user and benefactor.


Intelligent Empowerment: The Next Wave of Technology-led Disruption

#artificialintelligence

Technology is leading a new wave of disruption in our society. While powerful governments are worried about the potential implications of "intelligent" systems and robots displacing jobs, we're seeing more examples of such systems enabling business transformations. Intelligent Empowerment is a shift that brings together the best of both worlds: augmenting human intelligence with machine intelligence through the use of data and techniques such as Optimization, Artificial Intelligence, and Machine Learning. The market opportunity is real, with reports of AI being a 15 billion dollar industry, projected to rise to over 70 billion by 2020. IBM alone is investing 3 billion dollars to bring their Cognitive Computing to the Internet of Things (IoT).


[session] #MachineLearning and #BigData @CloudExpo #IoT #AI #ML #DL

#artificialintelligence

Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at 20th Cloud Expo, Ed Featherston, director / senior enterprise architect at Collaborative Consulting, will discuss the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine. Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at Cloud Expo, Ed Featherston, a director and senior enterprise architect at Collaborative Consulting, discussed the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine. Speaker Bio Ed Featherston is a director/senior enterprise architect at Collaborative Consulting.


[slides] #MachineLearning All About the Data @CloudExpo #BigData #AI #ML

#artificialintelligence

Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at Cloud Expo, Ed Featherston, a director and senior enterprise architect at Collaborative Consulting, discussed the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine. Speaker Bio Ed Featherston is a director/senior enterprise architect at Collaborative Consulting. He brings 35 years of technology experience in designing, building, and implementing large complex solutions. He has significant expertise in systems integration, Internet/intranet, and cloud technologies, Ed has delivered projects in various industries, including financial services, pharmacy, government and retail.