Welcome!

@CloudExpo Authors: Yeshim Deniz, Kevin Benedict, Pat Romanski, Liz McMillan, Elizabeth White

Related Topics: @CloudExpo, @DXWorldExpo, @ThingsExpo

@CloudExpo: Article

It’s All About the Data: #MachineLearning | @CloudExpo #IoT #ML #BigData

The goal of machine learning sounds simple: provide systems with the ability to learn based on the information provided them

Big Data. Analytics. Internet of Things. Cloud. In the last few years, you cannot have a discussion around technology without those terms entering the conversation. They have been major technology disruptors impacting all aspects of the business. Change seems to occur at breakneck speeds and shows no sign of slowing. Today, it appears the one constant in technology is change. Constant change requires constant innovation which thereby introduces more new technologies. One of the new technologies entering the conversation is machine learning. Gartner identified machine learning as one of the top 10 technology trends for 2016. It is definitely a hot topic.

Everything old is new again
What I find fascinating about machine learning is that the basic tenets harken back to the '70s and '80s in the early years of artificial intelligence research. The work at that time was constrained by compute capacity and amount of data available. This is the key that has enabled machine learning to leap forward in recent years - both of those constraints no longer hold. Compute cycles and data are available at levels unimagined just decades ago.

The goal of machine learning sounds simple: provide systems with the ability to learn based on the information provided them. Simple as it sounds, this is counter to classic software engineering and has its challenges. Most software development we are familiar with ‘hard codes' the systems behavior based on planned and anticipated user and data interactions. The standard ‘if-then-else' model.

The algorithms required for artificial intelligence/machine learning are much more complex. They need to allow the system to develop its own analytical models based on inputs. Those models are constantly changing based on the information provided. Based on the data and those models, behavior is determined. As you can tell from the description, this results in very non-deterministic behavior. The system will analyze, interpret and react based on the information provided, modify that behavior as more information, and then feedback is provided. The analysis and behavior is constantly changing and being refined over time. Imagine developing the test suite for that system! (A topic for future discussion).

You are already reaping the benefits of machine learning
Do you have a Netflix account? Or Amazon? Both Netflix and Amazon provide a ‘recommended for you' list every time you log in. Both companies have very complex, proprietary algorithms analyzing the huge repository of information about you and all their member's transactions. Based on that information, they develop models of your expected behavior and present a list of recommendations to you. How you react to those recommendations is also fed back into the algorithms, constantly tweaking and adjusting your behavior model.

Or how about your smart phone? Think for a moment about the complexity of the simple statement, "Siri, what is the weather forecast for today?" First the software needs to be able to understand your voice, your accent, and your way of speech in order to be able to determine the actual words being spoken. If it's not sure, the software asks for clarification, and it learns from the clarification. Each time you use it, your phone gets better at understanding what you are saying. Once it understands the words, it has to process natural language into something meaningful to the system. This again requires complex algorithms analyzing the information, creating a model, and executing on its interpretation. As with parsing the words, if it's not sure, the software will prompt for clarification. That clarification will be fed back into the system that models your way of speaking and the context of the language you use.

It's all about the data
In a recent article on TechCrunch, ‘How startups can compete with enterprises in artificial intelligence and machine learning' John Melas-Kyriazi refers to data as the ‘fuel we feed into training machine learning models that can create powerful network effects at scale.' I find that a very apt analogy. The complex algorithms and models are the engine of machine learning, but without fuel, the engine - the data - won't work very well, if at all. A colleague of mine, John Williams, (Chief Strategy Officer at Collaborative Consulting) for years has been fond of saying, "It's all about the data." That could not be more true than in the world of machine learning.

Given the importance of the data to the success of any machine learning implementation, there are some key considerations to take into account:

  • Data Quality - In the world of data, this has always been an important consideration. Data cleansing and scrubbing is standard practice already in many organizations. It has become critical for machine learning implementations. Putting dirty fuel into even the best engine will bring it to a grinding halt.
  • Data Volume - Big Data is tailor-made for machine learning. The more information the algorithms and subsequent models have to work with, the better the results. The key word here is learning. We as individuals learn more as more information is provided to us. This concept is directly applicable in the machine learning world.
  • Data Timeliness - Besides volume, new and timely data is also a consideration. If the machine learning is based on a large volume of data that is completely outdated, the resulting models will not be very useful.
  • Data Pedigree - Where did the data come from? Is it a valid source? The pedigree is less important when using internal systems, as the source is well known, but many machine learning systems will be getting their data from public sources. Or potentially, from the many devices in the world of the Internet of Things. Crowd-sourcing data (for example Waze, a GPS mobile app) requires extra effort to ensure you trust the information being consumed. Imagine a new kind of cyber-attack - feeding your machine learning system bad data to impact the results. Remember Microsoft's problem with its AI Chatbot Tay learning to be a racist?

No technology negates the need for good design and planning
There is no doubt machine learning technology has amazing potential at impacting businesses across the spectrum, whether it will be in healthcare for diagnosing Alzheimer's disease to self-driving cars that were once in the realm of science fiction. No technology negates the need for good design and planning; machine learning is no different. As technologists, it's our responsibility to ensure the proper efforts have been made to supply machine learning implementations with the best fuel possible. Understanding the quality, volume, timeliness, and pedigree needs of these systems can help us navigate this new world of machine learning, leading us to successful execution, and, ultimately, providing value back to the business.

More Stories By Ed Featherston

Ed Featherston is VP, Principal Architect at Cloud Technology Partners. 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. He has delivered projects in various industries, including financial services, pharmacy, government and retail.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@CloudExpo Stories
We are seeing a major migration of enterprises applications to the cloud. As cloud and business use of real time applications accelerate, legacy networks are no longer able to architecturally support cloud adoption and deliver the performance and security required by highly distributed enterprises. These outdated solutions have become more costly and complicated to implement, install, manage, and maintain.SD-WAN offers unlimited capabilities for accessing the benefits of the cloud and Internet. ...
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.
Headquartered in Plainsboro, NJ, Synametrics Technologies has provided IT professionals and computer systems developers since 1997. Based on the success of their initial product offerings (WinSQL and DeltaCopy), the company continues to create and hone innovative products that help its customers get more from their computer applications, databases and infrastructure. To date, over one million users around the world have chosen Synametrics solutions to help power their accelerated business or per...
Founded in 2000, Chetu Inc. is a global provider of customized software development solutions and IT staff augmentation services for software technology providers. By providing clients with unparalleled niche technology expertise and industry experience, Chetu has become the premiere long-term, back-end software development partner for start-ups, SMBs, and Fortune 500 companies. Chetu is headquartered in Plantation, Florida, with thirteen offices throughout the U.S. and abroad.
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.
In an era of historic innovation fueled by unprecedented access to data and technology, the low cost and risk of entering new markets has leveled the playing field for business. Today, any ambitious innovator can easily introduce a new application or product that can reinvent business models and transform the client experience. In their Day 2 Keynote at 19th Cloud Expo, Mercer Rowe, IBM Vice President of Strategic Alliances, and Raejeanne Skillern, Intel Vice President of Data Center Group and ...
More and more brands have jumped on the IoT bandwagon. We have an excess of wearables – activity trackers, smartwatches, smart glasses and sneakers, and more that track seemingly endless datapoints. However, most consumers have no idea what “IoT” means. Creating more wearables that track data shouldn't be the aim of brands; delivering meaningful, tangible relevance to their users should be. We're in a period in which the IoT pendulum is still swinging. Initially, it swung toward "smart for smart...
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 the strategy and defining the Big Data service offerings and capabilities for EMC Global Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He's written several white papers, is an avid blogge...
DXWorldEXPO LLC announced today that Dez Blanchfield joined the faculty of CloudEXPO's "10-Year Anniversary Event" which will take place on November 11-13, 2018 in New York City. Dez is a strategic leader in business and digital transformation with 25 years of experience in the IT and telecommunications industries developing strategies and implementing business initiatives. He has a breadth of expertise spanning technologies such as cloud computing, big data and analytics, cognitive computing, m...
"DivvyCloud as a company set out to help customers automate solutions to the most common cloud problems," noted Jeremy Snyder, VP of Business Development at DivvyCloud, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
"Venafi has a platform that allows you to manage, centralize and automate the complete life cycle of keys and certificates within the organization," explained Gina Osmond, Sr. Field Marketing Manager at Venafi, in this SYS-CON.tv interview at DevOps at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
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 the strategy and defining the Big Data service offerings and capabilities for EMC Global Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He's written several white papers, is an avid blogge...
We all know that end users experience the Internet primarily with mobile devices. From an app development perspective, we know that successfully responding to the needs of mobile customers depends on rapid DevOps – failing fast, in short, until the right solution evolves in your customers' relationship to your business. Whether you’re decomposing an SOA monolith, or developing a new application cloud natively, it’s not a question of using microservices – not doing so will be a path to eventual b...
In his session at 21st Cloud Expo, James Henry, Co-CEO/CTO of Calgary Scientific Inc., introduced you to the challenges, solutions and benefits of training AI systems to solve visual problems with an emphasis on improving AIs with continuous training in the field. He explored applications in several industries and discussed technologies that allow the deployment of advanced visualization solutions to the cloud.
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
HyperConvergence came to market with the objective of being simple, flexible and to help drive down operating expenses. It reduced the footprint by bundling the compute/storage/network into one box. This brought a new set of challenges as the HyperConverged vendors are very focused on their own proprietary building blocks. If you want to scale in a certain way, let's say you identified a need for more storage and want to add a device that is not sold by the HyperConverged vendor, forget about it...
"IBM is really all in on blockchain. We take a look at sort of the history of blockchain ledger technologies. It started out with bitcoin, Ethereum, and IBM evaluated these particular blockchain technologies and found they were anonymous and permissionless and that many companies were looking for permissioned blockchain," stated René Bostic, Technical VP of the IBM Cloud Unit in North America, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Conventi...
In this presentation, you will learn first hand what works and what doesn't while architecting and deploying OpenStack. Some of the topics will include:- best practices for creating repeatable deployments of OpenStack- multi-site considerations- how to customize OpenStack to integrate with your existing systems and security best practices.
Michael Maximilien, better known as max or Dr. Max, is a computer scientist with IBM. At IBM Research Triangle Park, he was a principal engineer for the worldwide industry point-of-sale standard: JavaPOS. At IBM Research, some highlights include pioneering research on semantic Web services, mashups, and cloud computing, and platform-as-a-service. He joined the IBM Cloud Labs in 2014 and works closely with Pivotal Inc., to help make the Cloud Found the best PaaS.
The “Digital Era” is forcing us to engage with new methods to build, operate and maintain applications. This transformation also implies an evolution to more and more intelligent applications to better engage with the customers, while creating significant market differentiators. In both cases, the cloud has become a key enabler to embrace this digital revolution. So, moving to the cloud is no longer the question; the new questions are HOW and WHEN. To make this equation even more complex, most ...
As you move to the cloud, your network should be efficient, secure, and easy to manage. An enterprise adopting a hybrid or public cloud needs systems and tools that provide: Agility: ability to deliver applications and services faster, even in complex hybrid environments Easier manageability: enable reliable connectivity with complete oversight as the data center network evolves Greater efficiency: eliminate wasted effort while reducing errors and optimize asset utilization Security: implemen...