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AI Is Not “Fake” Intelligence | @ExpoDX @Schmarzo #DX #ArtificialIntelligence
The word ‘artificial’ may not be the right term to use to describe ‘Artificial Intelligence’

Quick quiz!

What’s the first thing that comes to mind when you hear the following phrases?

  • Artificial grass
  • Artificial sweeteners
  • Artificial flavors
  • Artificial plants
  • Artificial flowers
  • Artificial diamonds and jewelry
  • Artificial (fake) news

These phrases probably evoke thoughts such as “fake,” “not real,” or even “shabby.” Artificial is such a harsh adjective. The word “artificial” is defined as “imitation; simulated; sham” with synonyms such as fake, false, mock, counterfeit, bogus, phony and factitious.

The word “artificial” may not be the right term to use to describe “Artificial Intelligence,” because “artificial intelligence” is anything but fake, false, phony, or a sham. Maybe a better term is Augmented Human Intelligence, or a phrase that highlights both the importance of augmenting the human’s intelligence as well as to alleviate the fears that AI means humans become “meat popsicles” (quick, name that Bruce Willis movie reference!). And while I don’t expect this name change to stick (if it does, please give me some credit), I’m using this blog as an excuse to introduce some marvelous new training materials on artificial intelligence and machine learning.

But before I dive into details, let’s first frame the artificial intelligence conversation.

Focusing on the “How” Won’t Lead You to the “What” and “Why”
Organizations have access to a growing variety of internal and external data sources that might yield better predictors of business performance. And while having a process to ideate, validate and prioritize the different data sources that one might want to explore for its predictive capabilities, in the end the data by itself is of little value – organizations need to become more effective at leveraging data and analytics to power their business models (see Figure 1).

Figure 1: Big Data Business Model Maturity Index

But in order to “monetize” that growing bounty of data, you’re going to need to become an expert at advanced analytics to tease out the customer, product, service, and operational insights that are the real sources of economic value (see University of San Francisco “Determining The Economic Value of Data” research paper). Business leaders need to become knowledgeable about advanced analytics capabilities so that they can envision “What” business use cases to target and “Why,” before they get pulled into the “How” discussion.

Preparing for the “How” Discussion
To help business leaders understand where and how to apply the different classes of advanced analytics (i.e., machine learning, neural networks, reinforcement learning, artificial intelligence), I’ve created an advanced analytics roadmap. I then mapped the advanced analytics roadmap against the Big Data Business Model Maturity Index (see Figure 2).

Figure 2: The Path for Creating the Intelligent Enterprise

While certainly not perfect (and certainly not definitive given continued advanced analytics advancements), Figure 2 attempts to classify the different advanced analytics capabilities into a roadmap that organizations can use to help them understand when and where to apply the different advanced analytics capabilities. This is my attempt to try to summarize the advanced analytics confusion, hype and excitement into something actionable.

With that as my goal, here are the different levels of advanced analytics:

  • Level 1: Insights and Foresight. This is the foundational level that includes statistical analytics as well as the broad categories of predictive analytics (e.g., clustering, classification, regression) and data mining. The goal of the level 1 is to quantify cause-and-effect, establish confidence levels and measure goodness of fit.
  • Level 2: Optimized Human-decision Making. This level includes machine learning, deep learning and neural networks. The goal of these advanced analytic algorithms is to enable computers to learn on their own; to identify patterns in data, build models that explain the data, and predict outcomes without having pre-programmed rules and analytic models.
  • Level 3: The Learning and Intelligent Enterprise. This level includes artificial intelligence, reinforcement learning and cognitive computing. These advanced analytic algorithms self-monitor, self-diagnose, self-adjust and self-learn. These analytics perceive the world around them, create goals, make decisions towards those goals, measure decision effectiveness, and learn in order to refine the decisions that advance towards the goals (maximize rewards while minimizing costs).

It is important to be able to summarize and present the wide realm of advanced analytics within a frame that we can explain to business leadership (because eventually we’re going to come to them for money). So using Figure 2 as our business framework, let’s deep dive into each of the advanced analytics levels.

Level 1: Insights and Foresights
The goal of Level 1 is to quantify “cause-and-effect” (i.e., quantify relationships in the data) and predict what is likely to happen at some measureable level of confidence. Level 1 sets the foundation for determining “goodness of fit,” or the extent to which observed data matches the values predicted by analytic models. Level 1 includes the following advanced analytic capabilities:

  • Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation and organization of data. Statistical analytics and methods are used to support hypotheses (decisions) and provide credibility to modeling results and outcomes (via confidence levels and “goodness of fit” measures). Check out “Statistics for Dummies Cheat Sheet” for more information about different statistical techniques.
  • Predictive Analytics and Data Mining include anomaly detection, clustering, classification, regression and association rule learning. Predictive analytics and data mining uncover statistically significant patterns in large data sets; they uncover relationships buried in the data in order to quantify risks and opportunities. Check out “23 Types of Regression” to see the different types of regression techniques available to the data scientist.

Level 2: Augmented Human Decision-making
Level 2 builds upon the predictions created in Level 1 in order to prescribe actions and recommendations. Level 2 is the domain of analytic capabilities focused on natural language processing (NLP), text translation, voice recognition, and photo/image/facial recognition. Advanced analytic capabilities in level 2 focus on learning and then making inferences from that learning. Level 2 includes the following analytic capabilities:

  • Neural Networks and Deep Learning leverage a system of highly interconnected analytic layers to decompose complex data formats (e.g., images, audio, video) in order to learn about the data and create inferences about the data. For example, Figure 3 shows how a series of interconnected neural network layers work to identify a written number.

Figure 3: Why Convolutional Neural Networks (Source URL provided below)

But beware, as there is not just one neural network technique, as can be seen in Figure 4.

Figure 4: The Asimov Institute, The Neural Network Zoo (Source URL provided below)

Machine Learning empowers systems and applications with the ability to gain knowledge without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. Machine Learning algorithms identify patterns in observed data, build models that explain the world, and predict things without having to explicitly pre-program rules and analytic models (see Figure 5).

Figure 5: The Difference Between Deep Learning Training and Inference (Source URL provided below)

Fundamentally, Machine Learning does two things: 1) quantifies relationships in the data (quantify relationships from historical data and apply those relationships to new data sets), and 2) quantifies latent relationships (draw inferences) buried in the data.

There are two types of machine learning:

  • Supervised machine learning is a type of machine learning algorithm used to draw inferences from data sets with label responses such as fraud, customer attrition, purchase transaction, part failure, social media engagement, or web click.
  • Unsupervised machine learning is a type of machine learning algorithm used to draw inferences from data sets without labeled responses such as finding hidden (unknown) patterns, groupings or relationships in data.

See the blog “Top 10 Machine Learning Algorithms” for detailed list of machine learning algorithms.

  • Adversarial Machine Learning is a fairly new area of machine learning. Adversarial Machine Learning sits at the intersection of machine learning and computer security. It seeks to enable the safe adoption of machine learning techniques in adversarial settings like spam filtering, malware detection and biometric recognition. Machine learning techniques were originally designed for stationary environments in which the training and test data are assumed to be generated from the same distribution. However in the presence of intelligent and adaptive adversaries, this working hypothesis is likely to be violated. For example, a malicious adversary can carefully manipulate the input data exploiting specific vulnerabilities of learning algorithms to compromise the whole system security.
  • Finally, Ensemble machine learning combines several machine learning techniques into one predictive model in order to decrease variance, bias, or improve predict effectiveness. Ensemble methods can be divided into two groups:
    • Sequential ensemble methods where the base learners are generated sequentially. The basic motivation of sequential methods is to exploit the dependence between the base learners. Weighing previously mislabeled examples with higher weight can boost the overall performance.
    • Parallel ensemble methods where the base learners are generated in parallel (e.g. Random Forest). The basic motivation of parallel methods is to exploit independence between the base learners since the error can be reduced dramatically by averaging.

See the article “Ensemble Learning to Improve Machine Learning Results” for more details on ensemble machine learning.

Level 3: The Learning and Intelligent Enterprise
Level 3 focuses on creating an intelligent enterprise that can self-monitor, self-diagnose, self-correct and self-learn. Level 3 is the domain of continuous “learning and adjusting” advanced analytic techniques such as reinforcement learning, artificial intelligence and cognitive computing. Level 3 includes the following analytic capabilities:

  • Reinforcement Learning focuses on how software agents take actions in an environment so as to maximize cumulative rewards while minimizing costs. Reinforcement learning uses trial-and-error to map situations to actions so as to maximize rewards. Actions may affect immediate rewards but actions may also affect subsequent or longer-term rewards, so the full extent of rewards must be considered when evaluating the reinforcement learning effectiveness. Reinforcement learning is used to address two general problems:
    • Prediction: How much reward can be expected for every combination of possible future states
    • Control: By moving through all possible combinations of the environment, find a combination of actions that maximizes reward and allows for optimal control

See “Transforming from Autonomous to Smart: Reinforcement Learning Basics” for more details on reinforcement learning.

  • Artificial Intelligence is the ability for a computer system to acquire knowledge within a particular environment, apply the knowledge to successfully interact within that environment, and learn from the resulting interaction so that subsequent interactions get more effective, even to the point where an artificial intelligent application could re-program itself to more successfully perform (survive?) within a complex environment or situation (now that should scare the singularity folks[4]).

Artificial intelligence involves the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. An intelligent agent is an autonomous entity that observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is “rational,” as defined in economics). There are 4 general types of intelligent agents:

  • Simple reflex agents
  • Model-based reflect agents
  • Goal-based reflect agents
  • Utility-based reflect agents

Figure 6: Simple Reflect Agent (Source URL provided below)

Cognitive Computing is a relatively new concept that is being championed by IBM Watson. Cognitive computing involves self-learning systems that simulate human thought processes and decision-making in complex situations. From Wikipedia, we get cognitive systems features including:

  • Adaptive: may learn as information changes, and as goals and requirements evolve
  • Interactive: may interact easily with users so that those users can define their needs comfortably
  • Iterative: may aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete
  • Contextual: may understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task and goals

Summary
You can’t get to the “What” and the “Why” by focusing on the “How”

It is also important to understand the “How” in order to envision the “What” and “Why.” Sometimes the wide variety of advanced analytic techniques and algorithms cause confusion, and cause business leaders to slow down or even stop until they understand these advanced analytic capabilities better. The goal of this blog was to provide enough of an explanation of advanced analytics to business leaders so that when we get engaged in an envisioning exercise, we get turn off the governors that limit creative thinking.

Appendix: Marvelous Sources of Advanced Analytics Knowledge
There are many sources of excellent education available on advanced analytics, such as Andrew Ng’s deep learning classes on Coursera. One of my favorites is the content provided by the “Machine Learning for Humans” site. It has excellent material and includes a free downloadable e-book.

Figure 7: Machine learning is one of many subfields of artificial intelligence, concerning the ways that computers learn from experience to improve their ability to think, plan, decide, and act.

I’ll continue to share new sources of great educational material on advanced analytics as they get released into the wilds. Understand the “how” will help organizations to envision the realm of what’s possible. Many times, that envisioning is only limited by the organizations creativity and management commitment.

Sources:

Figure 3: Why Convolutional Neural Networks

Figure 4: The Asimov Institute – The Neural Network Zoo

Figure 5: Nvidia – What’s the Difference Between Deep Learning Training and Inference?

[4] The technological singularity is the hypothesis that the invention of artificial super intelligence will abruptly trigger runaway technological growth, resulting in unfathomable changes to human civilization (a.k.a. Skynet).

Figure 6: Philosophy of Artificial Intelligence: Simple Reflex Agent

The post Artificial Intelligence is not “Fake” Intelligence appeared first on InFocus Blog | Dell EMC Services.


DXWorldEXPO LLC, the producer of the world's most influential technology conferences and trade shows has announced the conference tracks for CloudEXPO | DXWorldEXPO 2018 New York.

DXWordEXPO New York 2018, colocated with CloudEXPO New York 2018 will be held November 11-13, 2018, in New York City.

Digital Transformation (DX) is a major focus with the introduction of DXWorldEXPO within the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term.

A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throughout enterprises of all sizes.

Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

Sponsorship Opportunities Here

Speaking Opportunities Here

Sponsorship and Speaking Inquiries: info@dxworldexpo.com.

2018 Conference Agenda, Keynotes and 10 Conference Tracks

DXWordEXPO New York 2018 and Cloud Expo New York 2018 agenda present 222 rockstar faculty members, 200 sessions and 22 keynotes and general sessions in 10 distinct conference tracks.

  • Cloud-Native | Serverless
  • DevOpsSummit
  • FinTechEXPO - New York Blockchain Event
  • CloudEXPO - Enterprise Cloud
  • DXWorldEXPO - Digital Transformation (DX)
  • Smart Cities | IoT | IIoT
  • AI | Machine Learning | Cognitive Computing
  • BigData | Analytics
  • The API Enterprise | Mobility | Security
  • Hot Topics | FinTech | WebRTC

Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

DXWorldEXPO | CloudEXPO 2018 New York cover all of these tools, with the most comprehensive program and with 222 rockstar speakers throughout our industry presenting 22 Keynotes and General Sessions, 200 Breakout Sessions along 10 Tracks, as well as our signature Power Panels. Our Expo Floor brings together the world's leading companies throughout the world of Cloud Computing, DevOps, FinTech, Digital Transformation, and all they entail.

As your enterprise creates a vision and strategy that enables you to create your own unique, long-term success, learning about all the technologies involved is essential. Companies today not only form multi-cloud and hybrid cloud architectures, but create them with built-in cognitive capabilities.

Cloud-Native thinking is now the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, as well as the public sector.

CloudEXPO is the world's most influential technology event where Cloud Computing was coined over a decade ago and where technology buyers and vendors meet to experience and discuss the big picture of Digital Transformation and all of the strategies, tactics, and tools they need to realize their goals.

FinTech Is Now Part of the DXWorldEXPO | CloudEXPO Program!

Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses.

Accordingly, attendees at the upcoming 22nd CloudEXPO | DXWorldEXPO November 11-13, 2018 in New York City will find fresh new content in two new tracks called:

  • FinTechEXPO
  • New York Blockchain Event

which will incorporate FinTech and Blockchain, as well as machine learning, artificial intelligence and deep learning in these two distinct tracks.

Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

Sponsorship Opportunities Here

Speaking Opportunities Here

Sponsorship and Speaking Inquiries: info@dxworldexpo.com.

FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.

More than US$20 billion in venture capital is being invested in FinTech this year. DXWorldEXPOCloudEXPO are pleased to bring you the latest FinTech developments as an integral part of our program.

DXWorldEXPO | CloudEXPO are accepting speaking submissions for this new track, so please visit Cloud Computing Expo for the latest information or contact us at info@dxworldexpo.com.

Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

Sponsorship Opportunities Here

Speaking Opportunities Here

Sponsorship and Speaking Inquiries: info@dxworldexpo.com.

Download Slide Deck ▸ Here

Only DXWorldEXPO | CloudEXPO bring together all this in a single location:

Attend DXWorldEXPO | CloudEXPO. Build your own custom experience. Learn about the world's latest technologies and chart your course to Digital Transformation.

22nd International DXWorldEXPO | CloudEXPO, taking place November 11-13, 2018, in New York City, will feature technical sessions from a rock star conference faculty and the leading industry players in the world.

Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

Sponsorship Opportunities Here

Speaking Opportunities Here

Sponsorship and Speaking Inquiries: info@dxworldexpo.com.

Download Slide Deck: ▸ Here

Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy. Meanwhile, 94% of enterprises are using some form of XaaS - software, platform, and infrastructure as a service.

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

Every Global 2000 enterprise in the world is now integrating cloud computing in some form into its IT development and operations. Midsize and small businesses are also migrating to the cloud in increasing numbers.

Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)

Sponsorship Opportunities Here

Speaking Opportunities Here

Sponsorship and Speaking Inquiries: info@dxworldexpo.com.

Download Slide Deck: ▸ Here

Companies are each developing their unique mix of cloud technologies and services, forming multi-cloud and hybrid cloud architectures and deployments across all major industries. Cloud-driven thinking has become the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, and the public sector.

Sponsorship Opportunities

DXWorldEXPO | CloudEXPO are the single show where technology buyers and vendors can meet to experience and discus cloud computing and all that it entails. Sponsors of DXWorldEXPO | CloudEXPO will benefit from unmatched branding, profile building and lead generation opportunities through:

  • Featured on-site presentation and ongoing on-demand webcast exposure to a captive audience of industry decision-makers.
  • Showcase exhibition during our new extended dedicated expo hours
  • Breakout Session Priority scheduling for Sponsors that have been guaranteed a 35-minute technical session
  • Online advertising on 4,5 million article pages in SYS-CON's i-Technology Publications
  • Capitalize on our Comprehensive Marketing efforts leading up to the show with print mailings, e-newsletters and extensive online media coverage.
  • Unprecedented PR Coverage: Unmatched editorial coverage on Cloud Computing Journal.
  • Tweetup to over 100,000 plus Twitter followers
  • Press releases sent on major wire services to over 500 industry analysts.

Secrets of Our Most Popular Sponsors and Exhibitors ▸ Here

For more information on sponsorship, exhibit, and keynote opportunities, contact info@dxworldexpo.com.

Sponsorship Opportunities Here

Download Slide Deck:Here

Speaking Opportunities

The upcoming 22nd International DXWorldEXPO | CloudEXPO November 11-13, 2018 in New York City, NY announces that its Call For Papers for speaking opportunities is now open.

Secrets of Our Most Popular Faculty Members ▸ Here

Submit your speaking proposal Here or by email info@dxworldexpo.com.

Download Slide Deck: ▸ Here

About DXWorldEXPO LLC

DXWorldEXPO LLC is a Lighthouse Point, Florida-based trade show company and the creator of DXWorldEXPODigital Transformation Conference & Expo. The company produces and presents CloudEXPO, DevOpsSummitFinTechEXPO Blockchain Event, the world's most influential conferences and trade shows.

About William Schmarzo
Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

Articles & Feature Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed how this same philosophy can be applied to highly scaled applications, and can dramatically increase your resilience to failure.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by sharing information within the building and with outside city infrastructure via real time shared cloud capabilities.
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regulatory scrutiny and increasing consumer lack of trust in technology in general.
CI/CD is conceptually straightforward, yet often technically intricate to implement since it requires time and opportunities to develop intimate understanding on not only DevOps processes and operations, but likely product integrations with multiple platforms. This session intends to bridge the gap by offering an intense learning experience while witnessing the processes and operations to build from zero to a simple, yet functional CI/CD pipeline integrated with Jenkins, Github, Docker and Azure.
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a member of the Society of Information Management (SIM) Atlanta Chapter. She received a Business and Economics degree with a minor in Computer Science from St. Andrews Presbyterian University (Laurinburg, North Carolina). She resides in metro-Atlanta (Georgia).
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.
Containers and Kubernetes allow for code portability across on-premise VMs, bare metal, or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this session we'll describe best practices for "configuration as code" in a Kubernetes environment. We will demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure using the Kublr platform, and how Kubernetes objects, such as persistent volumes, ingress rules, and services, can be used to abstract from the infrastructure.
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Cloud Expo - Cloud Looms Large on SYS-CON.TV



Cloud Expo 2009 Europe Opening Keynote by GoodData

In this presentation Roman Stanek, a technology visionary who has spent the past fifteen years building world-class technology companies, will talk about what it means to be 'born on the cloud.' Specifically Roman will share with delegates his thoughts on how to use cloud computing as a technical design center; how to take advantage of the economics of cloud computing in building and operating cloud services; how to dramatically change customer adoption; and how to plan for the technical and operational scale that cloud computing makes possible.

Good Data - Collaborative Analytics On Demand
This presentation will describe how Good Data moved from concept to reality, demonstrating the importance of architecting for the cloud: how to conceptualize, design and build applications when cloud computing is a given.

VMware - Building Cloud Infrastructures with VMware vSphere
vSphere 4 is the industry’s first cloud operating system, transforming datacenters into dramatically simplified environments to enable the next generation of flexible, reliable IT services. Combining VMware’s industry leading virtualization technology and experience, VMware vSphere delivers uncompromising control, with greater efficiency, while preserving customer choice.

Sun Microsystems - The Sun Cloud: Sun's Public Cloud Computing Service
Cloud Computing is empowering users like never before, giving them access to massive amounts of compute power and storage capacity, on- demand and in real time. This session will outline Sun's vision and strategy for cloud computing, highlighting the Sun Cloud - Sun's Public Cloud Service that leverages a broad range of Sun's innovative hardware and software technology and products. An overview presentation and practical demonstrations / demos will showcase how cloud-based compute and storage resources enable users to deploy applications quickly, easily and inexpensively..

The Time is Right for Enterprise Cloud Computing
During his keynote, Rich Marcello, Senior Vice President of Unisys, will discuss the latest technologies and approaches that help knock down these barriers, creating the opportunity for attendees to now consider cloud managed services as part of their data center journey to secure "IT as a Service".

Accelerating Innovation with Cloud Computing
Join Shelton Shugar, Senior Vice President of Cloud Computing at Yahoo! for a keynote elaborating on how Yahoo! and consumers benefit from Yahoo! Cloud Services and will describe Yahoo! Cloud Services and technologies.

CloudEXPO Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed how this same philosophy can be applied to highly scaled applications, and can dramatically increase your resilience to failure.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by sharing information within the building and with outside city infrastructure via real time shared cloud capabilities.
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
Top Stories for Cloud Expo Europe

Kevin L Jackson launched the "Government Cloud Computing Journal" on Ulitzer. The online magazine offers stories and articles on the effective use of cloud computing technologies within the government domain. Kevin L. Jackson is a senior information technologist specializing in information technology solutions that meet critical Federal government operational requirements. Currently, he serves as Director, Business Development for Dataline, Inc., and editor of Government Cloud Computing e-zine. Kevin L. Jackson (right) with Cloud Computing Expo conference chair Jeremy Geelan before his presentation on Government Cloud Computing. About Ulitzer.com Initiating content coverage on any topic or launching a magazine at Ulitzer.com  is designed to be as easy as boiling an egg and doesn't take much longer. To become a Ulitzer author, anyone can fill out a simple author prof... (more)

Best Recent Articles on Cloud Computing & Big Data Topics
As we enter a new year, it is time to look back over the past year and resolve to improve upon it. In 2014, we will see more service providers resolve to add more personalization in enterprise technology. Below are seven predictions about what will drive this trend toward personalization.
IT organizations face a growing demand for faster innovation and new applications to support emerging opportunities in social, mobile, growth markets, Big Data analytics, mergers and acquisitions, strategic partnerships, and more. This is great news because it shows that IT continues to be a key stakeholder in delivering business service innovation. However, it also means that IT must deliver new innovation despite flat budgets, while maintaining existing services that grow more complex every day.
Cloud computing is transforming the way businesses think about and leverage technology. As a result, the general understanding of cloud computing has come a long way in a short time. However, there are still many misconceptions about what cloud computing is and what it can do for businesses that adopt this game-changing computing model. In this exclusive Q&A with Cloud Expo Conference Chair Jeremy Geelan, Rex Wang, Vice President of Product Marketing at Oracle, discusses and dispels some of the common myths about cloud computing that still exist today.
Despite the economy, cloud computing is doing well. Gartner estimates the cloud market will double by 2016 to $206 billion. The time for dabbling in the cloud is over! The 14th International Cloud Expo, co-located with 5th International Big Data Expo and 3rd International SDN Expo, to be held June 10-12, 2014, at the Javits Center in New York City, N.Y. announces that its Call for Papers is now open. Topics include all aspects of providing or using massively scalable IT-related capabilities as a service using Internet technologies (see suggested topics below). Cloud computing helps IT cut infrastructure costs while adding new features and services to grow core businesses. Clouds can help grow margins as costs are cut back but service offerings are expanded. Help plant your flag in the fast-expanding business opportunity that is The Cloud, Big Data and Software-Defined Networking: submit your speaking proposal today!
What do you get when you combine Big Data technologies….like Pig and Hive? A flying pig? No, you get a “Logical Data Warehouse.” In 2012, Infochimps (now CSC) leveraged its early use of stream processing, NoSQLs, and Hadoop to create a design pattern which combined real-time, ad-hoc, and batch analytics. This concept of combining the best-in-breed Big Data technologies will continue to advance across the industry until the entire legacy (and proprietary) data infrastructure stack will be replaced with a new (and open) one.
While unprecedented technological advances have been made in healthcare in areas such as genomics, digital imaging and Health Information Systems, access to this information has been not been easy for both the healthcare provider and the patient themselves. Regulatory compliance and controls, information lock-in in proprietary Electronic Health Record systems and security concerns have made it difficult to share data across health care providers.
Cloud Expo, Inc. has announced today that Vanessa Alvarez has been named conference chair of Cloud Expo® 2014. 14th International Cloud Expo will take place on June 10-12, 2014, at the Javits Center in New York City, New York, and 15th International Cloud Expo® will take place on November 4-6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
12th International Cloud Expo, held on June 10–13, 2013 at the Javits Center in New York City, featured four content-packed days with a rich array of sessions about the business and technical value of cloud computing led by exceptional speakers from every sector of the cloud computing ecosystem. The Cloud Expo series is the fastest-growing Enterprise IT event in the past 10 years, devoted to every aspect of delivering massively scalable enterprise IT as a service.
Ulitzer.com announced "the World's 30 most influential Cloud bloggers," who collectively generated more than 24 million Ulitzer page views. Ulitzer's annual "most influential Cloud bloggers" list was announced at Cloud Expo, which drew more delegates than all other Cloud-related events put together worldwide. "The world's 50 most influential Cloud bloggers 2010" list will be announced at the Cloud Expo 2010 East, which will take place April 19-21, 2010, at the Jacob Javitz Convention Center, in New York City, with more than 5,000 expected to attend.
It's a simple fact that the better sales reps understand their prospects' intentions, preferences and pain points during calls, the more business they'll close. Each day, as your prospects interact with websites and social media platforms, their behavioral data profile is expanding. It's now possible to gain unprecedented insight into prospects' content preferences, product needs and budget. We hear a lot about how valuable Big Data is to sales and marketing teams. But data itself is only valuable when it's part of a bigger story, made visible in the right context.
Cloud Expo, Inc. has announced today that Larry Carvalho has been named Tech Chair of Cloud Expo® 2014. 14th International Cloud Expo will take place on June 10-12, 2014, at the Javits Center in New York City, New York, and 15th International Cloud Expo® will take place on November 4-6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
Everyone talks about a cloud-first or mobile-first strategy. It's the trend du jour, and for good reason as these innovative technologies have revolutionized an industry and made savvy companies a lot of money. But consider for a minute what's emerging with the Age of Context and the Internet of Things. Devices, interfaces, everyday objects are becoming endowed with computing smarts. This is creating an unprecedented focus on the Application Programming Interface (API) as developers seek to connect these devices and interfaces to create new supporting services and hybrids. I call this trend the move toward an API-first business model and strategy.
We live in a world that requires us to compete on our differential use of time and information, yet only a fraction of information workers today have access to the analytical capabilities they need to make better decisions. Now, with the advent of a new generation of embedded business intelligence (BI) platforms, cloud developers are disrupting the world of analytics. They are using these new BI platforms to inject more intelligence into the applications business people use every day. As a result, data-driven decision-making is finally on track to become the rule, not the exception.
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The World's 30 Most influential Cloud Bloggers
Cloud Expo on Ulitzer
1
Dustin Amrhein 11 Kevin Hoffman 21 Greg O'Connor
2
Ezhil Babaraj 12 Alin Irimie 22 Maureen O'Gara
3
Tony Bishop 13 Kevin Jackson 23 Mark O'Neill
4
Reuven Cohen 14 Fuat Kircaali 24 Bill Roth
5
Ernest de Leon 15 David Linthicum 25 Ellen Rubin
6
David Dean 16 Lori MacVittie 26 John Savageau
7
Ray DePena 17 Bill McColl 27 Michael Sheehan
8
Dana Gardner 18 Paul Miller 28 Roman Stanek
9
John Gauntt 19 Louis Naugès 29 John Treadway
10
Jeremy Geelan 20 Greg Ness 30 Alan Williamson

Digital Transformation Blogs
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regulatory scrutiny and increasing consumer lack of trust in technology in general.
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a member of the Society of Information Management (SIM) Atlanta Chapter. She received a Business and Ec...
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2009west.sys-con.com

 
    Virtualization Expo West
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    GovIT Expo
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    Cloud Expo Europe
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Cloud Expo 2010 Allstar Conference Faculty

SARWAL
Oracle

COFFEE
Salesforce

KHAN
Sybase

BISHOP
Adaptivity

MALCOLM
Abiquo

KHALIDI
Microsoft

RILEY
AWS

AZUA
IBM

BARRETO
Intel

CHAKRAVARTY
Novell

CRANDELL
RightScale

GAUVIN
Virtual Ark

GROSS
Unisys

SCHALK
Google

YEN
Juniper Networks

WILLOUGHBY
Compuware

What The Enterprise IT World Says About Cloud Expo
 
"We had extremely positive feedback from both customers and prospects that attended the show and saw live demos of NaviSite's enterprise cloud based services."
  –William Toll
Sr. Director, Marketing & Strategic Alliances
Navisite
 


 
"More and better leads than ever expected! I have 4-6 follow ups personally."
  –Richard Wellner
Chief Scientist
Univa UD
 


 
"Good crowd, good questions. The event looked very successful."
  –Simon Crosby
CTO
Citrix Systems
 


 
"Great conference and group of speakers, interesting timely announcements, and awesome networking."
  –Ricardo Sanchez
Software Architect
Myriadtech