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  Think Big – Now Think Even Bigger
  Join Us at Internet of Things at Cloud Expo, November 11-13,
at the Javits Center!


The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago.

All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend Internet of Things at Cloud Expo in New York City. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as "IoT-Ready" as it can be!

Delegates to Internet of Things at Cloud Expo will be able to attend eight separate, information-packed tracks:

  • Enterprise Cloud
  • Digital Transformation
  • The API Enterprise | Mobility & Security
  • DevOps | Containers & Microservices
  • Cognitive Computing | AI, ML, DL
  • Big Data | Analytics
  • IoT | IIoT | Smart Cities
  • Hot Topics | FinTech | WebRTC

There are 120 breakout sessions in all, with Keynotes, General Sessions, and Power Panels adding to three days of incredibly rich presentations and content.


We'll see you in New York!



Day 3 Keynote at @ThingsExpo | Chris Matthieu, CTO of Octoblu
In his keynote at @ThingsExpo, Chris Matthieu, Director of IoT Engineering at Citrix and co-founder and CTO of Octoblu, focused on building an IoT platform and company. He provided a behind-the-scenes look at Octoblu's platform, business, and pivots along the way (including the Citrix acquisition of Octoblu).
  Themes & Topics to Be Discussed

Consumer IoT
• Wearables
• Smart Appliances
• Smart Cars
• Smartphones 2.0
• Automation
• Smart Travel
• Personal Fitness
• Health Care
• Personalized Marketing
• Customized Shopping
• Personal Finance
• The Digital Divide
• Mobile Cash & Markets
• Games & The IoT
• The Future of Education
• Virtual Reality

Enterprise IoT
• The Business Case for
x IoT
• Smart Grids
• Smart Cities
• Smart Transportation
• The Smart Home
• M2M
• Authentication/Security
• Wiring the IoT
• The Internet of
x Everything
• Digital Transformation
x of Enterprise IT
• Agriculture
• Transportation
• Manufacturing
• Local & State
x Government
• Federal Government

IoT Developers | WebRTC Summit
• Eclipse Foundation
• Cloud Foundry
• Linux Containers
• Node-Red
• Open Source Hardware
• Ajax and the IoT
• Leveraging SOA
• Multi-Cloud IoT
• Evolving Standards
• WebSockets
• Security & Privacy
x Protocols
• GPS & Proximity
x Services
• Bluetooth/RFID/etc
• XMPP
• Nest Labs



The Top Keynotes, the Best Sessions, a Rock Star Faculty and the Most Qualified Delegates of ANY Internet of Things Event!


The future of computing lies in these things. As computing takes a much more active role in our lives it will at the same time become much more invisible. Internet of Things Expo will address the challenges in getting from where we are today to this future.
 
The high-energy event is a must-attend for senior technologists from CEOs on down – including CIOs, CTOs, directors of infrastructure, VPs of technology, IT directors and managers, network and storage managers, network engineers, enterprise architects, and communications and networking specialists.




@ThingsExpo Power Panel | The World's Many IoTs: Which Are the Most Important?
The Internet of Things is clearly many things: data collection and analytics, wearables, Smart Grids and Smart Cities, the Industrial Internet, and more. Cool platforms like Arduino, Raspberry Pi, Intel's Galileo and Edison, and a diverse world of sensors are making the IoT a great toy box for developers in all these areas. In this Power Panel at @ThingsExpo, panelists discussed what things are the most important, which will have the most profound effect on the world, and what should we expect to see over the next couple of years.
Benefits of Attending the Three-Day Technical Program
  LEARNexactly why Internet of Things is relevant today from an economic, business and technology standpoint.
  HEAR first-hand from industry experts the common issues and requirements for creating a platform for the Internet of Things.
  SEE what new tools and approaches the Internet of Things requires.
  DISCOVER how to drive a distributed approach to the Internet of Things, where applications move to the data.
  FIND OUThow the vast volumes of new data produced by the Internet of Things provides a valuable new source of business insight through advanced analytical techniques.
  MASTER how the ongoing development of smart cities, cars, and houses will enhance connectivity infrastructure.
Lunch Power Panel | Microservices & IoT- Moderated by Jason Bloomberg
In this Power Panel at @DevOpsSummit, moderated by Jason Bloomberg, president of Intellyx, panelists Roberto Medrano, Executive Vice President at Akana; Lori MacVittie, Evangelist for F5 Networks; and Troy Topnik, ActiveState's Technical Product Manager; and Otis Gospodnetic, founder of Sematext; peeled away the buzz and discuss the important architectural principles behind implementing IoT solutions for the enterprise. As remote IoT devices and sensors become increasingly intelligent, they become part of our distributed cloud environment, and we must architect and code accordingly. At the very least, you'll have no problem filling in your buzzword bingo cards.


Machine Learning - Azure vs AWS By @SrinivasanSunda | @CloudExpo #IoT #Cloud
The importance of machine learning

Machine Learning - Azure vs AWS

Machine Learning, which is a process to predict future patterns and incidents based on the models created out of past data, is definitely the most important part of the success of the Internet of Things in the enterprise and consumer space. The main reason is that without machine learning the entire backbone of the Internet of Things - event acquisition, event processing , event storage and event reporting - is merely a live display of events happening elsewhere and will not provide any value to its consumers. Think of a smart monitor in an oil well that monitors various climatic conditions and other factors that can cause a failure; unless the monitor is able to predict of a failure and corrects itself the usage of such solution is quite limited.

MLPaaS - Azure Vs AWS
In that context, Machine Learning Platform as a Service (MLPaaS) has been a major component of the major cloud platforms. Both Azure and AWS have equivalent services, the below thoughts are comparison of major building blocks of a machine learning service and how the respective cloud providers handle them.

Machine Learning Component

Azure

Amazon AWS

Training Data Enablement: As the machine learning falls in to two major categories of Supervised Learning and Unsupervised Learning, proper training data is one of the most important aspect of a success of a machine learning experiment and how well a MLPaaS facilitates availability and usage of training data is a key factor.

Azure ML has extensive options for data input and manipulation. The Data sources could be any of, Hive, Azure SQL, Blob Storage, web based data feeding engines and even the data could be manually entered.

 

Never a input data from source could be directly used as a training data and hence in this context, Azure ML has an array of transformation functions like, Filter, Data Manipulation, Split and Reduce.

 

With the effective use of above options Azure ML will provide an effective means of integrating training data as part of the machine learning process.

AWS Machine Learning also supports multiple data sources within its eco system.

 

Amazon Simple Storage Service (Amazon S3) is storage for the AWS cloud platform. Amazon ML uses Amazon S3 as a

primary data repository.

 

Amazon ML allows you to create a data source object from data residing in Amazon Redshift, which is the Data Warehouse Platform as a service.

 

Amazon ML also allows you to create a datasource object from data stored in a MySQL database in Amazon

Relational Database Service (Amazon RDS).

 

Also Amazon ML provides a rich set of data transformation functions like, N-gram transformation, Orthogonal Sparse Bigram transformation and more.

Support For Machine Learning Life Cycle: Developing and consuming a machine learning model for an enterprise use case is in itself a eco system. There are multiple players like data scientist, data analyst, ETL Developers, Visualization Engineers and business users are involved and each one plays an important role. Hence any machine learning service should support this life cycle of work flow.

One of the key success factor of Azure ML is the positioning of Azure ML studio and its user friendly graphical interface and supporting workflows which makes the machine learning process highly collaborative and interactive.

The concept of Workspace nicely allows for separation of duties as well as seamless integration with rest of Azure eco system like storage. Typically Data scientist initially creates models and train them with various parameters and data combinations \. Also rich Visualization features help data scientist to test the results easily.

Once a model is trained successfully, Azure provides easy options to create a scoring experiment which can be ultimately published as a web service to be consumed by client applications.

The graphical interface of Amazon ML provides a very similar experience and features in terms of creating and training models.

 

While there is no separation between a training and scoring experiment, Amazon ML provides lot of options for model evaluation and interpretation.

 

When we evaluate an ML model, Amazon ML provides an industry-standard metric and a number of

insights to review the predictive accuracy of the model.

Algorithm Support: This is probably the most important piece of evaluating a machine learning service as there are different algorithms which can be applied for different situations.

While almost all machine learning solutions are covered under the three major categories namely, Clustering, Classification and Regression based on whether we needed a supervised machine learning or unsupervised machine learning.

However the real challenge could be the particular algorithm that suit the above 3 analysis categories.

Azure machine learning supports a whole array of algorithms be it, Decision Trees, Logistic Regression, Bayes Point Machine, Nerual Networks, K-Means ... to just name a few.

One important aspect of Azure machine learning is the democratization of these advanced algorithms that even without any programming knowledge of machine learning languages like R we could effectively deploy them for given use cases.

Amazon ML supports three types of ML models: binary classification, multiclass classification, and regression.

 

As the name indicates, Binary classification is used to predict one of two possible out comes.

 

Multi class classification is used to predict one of three or more possible out comes.

 

Regression is used to predict a continuous variable which is a number.

However as per documentation there does not seem to be an option within the Amazon ML to select individual algorithms like a K-Means as part of evaluating the model.

Consumer Applications: Once the model is trained it has to be put into the practice and the most natural usage is that the results of machine learning are to be used as part of consumer application and in todays context it is mostly a mobile based consumer. So a robust machine learning service should support multiple consumer applications too.

Azure machine learning provides ready to go client side code for the web services that are published. It supports clients for both request and response model as well as batch based execution. Azure machine learning also produces sample client side code in C#, Python and R. It provides an easy interface for testing the request and response parameters. When it comes to batch execution, Azure machine learning provides APIs for submitting and starting a job and sample code is available in C#, Python and R. With this support Azure machine learning provides excellent support for developing client side applications.

Amazon support both batch predictions as well as real time predictions with the support of API for each of the tasks.

 

Amazon ML API has batch prediction APIs like, Create, Update, Delete which can be used for creating batch applications.

 

Similarly the real time machine learning API samples are available in platforms like Java, Python and Scala.

Pricing aspects are not discussed in the table because PaaS solutions like machine learning are charged per usage and the pricing is either per prediction or by per prediction hour and typically enterprises would worry more about the capabilities of the platform in choosing a machine learning service.

Also without doing significant machine learning case studies we cannot comment on the algorithms and their support; however, a higher level view indicates that Azure Machine Learning supports more algorithms and individual choice of algorithms within a category like clustering, classification which may be of interest to seasoned data scientists. Also most data scientists predict the future of machine learning will be on unsupervised learning which has got a good support from Azure in the form clustering algorithms, especially the K-Means algorithm.

About Srinivasan Sundara Rajan
Highly passionate about utilizing Digital Technologies to enable next generation enterprise. Believes in enterprise transformation through the Natives (Cloud Native & Mobile Native).

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2016 | 2015 | 2014 | 2013 | 2012
Testimonials
This week I had the pleasure of delivering the opening keynote at Cloud Expo New York. It was amazing to be back in the great city of New York with thousands of cloud enthusiasts eager to learn about the next step on their journey to embracing a cloud-first worldl."
@SteveMar_Msft
 
How does Cloud Expo do it every year? Another INCREDIBLE show - our heads are spinning - so fun and informative."
@SOASoftwareInc
 
Thank you @ThingsExpo for such a great event. All of the people we met over the past three days makes us confident IoT has a bright future."
@Cnnct2me
 
One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

@Flexential


Who Should Attend?
Senior Technologists including CIOs, CTOs & Vps of Technology, Chief Systems Engineers, IT Directors and Managers, Network and Storage Managers, Enterprise Architects, Communications and Networking Specialists, Directors of Infrastructure.

Business Executives including CEOs, CMOs, & CIOs , Presidents & SVPs, Directors of Business Development , Directors of IT Operations, Product and Purchasing Managers, IT Managers.

Join Us as a Media Partner - Together We Can Enable the Digital Transformation!
SYS-CON Media has a flourishing Media Partner program in which mutually beneficial promotion and benefits are arranged between our own leading Enterprise IT portals and events and those of our partners.

If you would like to participate, please provide us with details of your website/s and event/s or your organization and please include basic audience demographics as well as relevant metrics such as ave. page views per month.

To get involved, email events@sys-con.com.

@ThingsExpo Blogs
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Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
Digital Transformation Blogs
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