Welcome!

Java IoT Authors: Liz McMillan, William Schmarzo, Stackify Blog, Kevin Benedict, XebiaLabs Blog

Related Topics: @CloudExpo, Mobile IoT, @ThingsExpo

@CloudExpo: Blog Post

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.

More Stories By 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).

@ThingsExpo Stories
From 2013, NTT Communications has been providing cPaaS service, SkyWay. Its customer’s expectations for leveraging WebRTC technology are not only typical real-time communication use cases such as Web conference, remote education, but also IoT use cases such as remote camera monitoring, smart-glass, and robotic. Because of this, NTT Communications has numerous IoT business use-cases that its customers are developing on top of PaaS. WebRTC will lead IoT businesses to be more innovative and address...
SYS-CON Events announced today that CA Technologies has been named "Platinum Sponsor" of SYS-CON's 21st International Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. CA Technologies helps customers succeed in a future where every business - from apparel to energy - is being rewritten by software. From planning to development to management to security, CA creates software that fuels transformation for companies in the applic...
In his opening keynote at 20th Cloud Expo, Michael Maximilien, Research Scientist, Architect, and Engineer at IBM, discussed the full potential of the cloud and social data requires artificial intelligence. By mixing Cloud Foundry and the rich set of Watson services, IBM's Bluemix is the best cloud operating system for enterprises today, providing rapid development and deployment of applications that can take advantage of the rich catalog of Watson services to help drive insights from the vast t...
Internet of @ThingsExpo, taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 21st Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. 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 devic...
Recently, IoT seems emerging as a solution vehicle for data analytics on real-world scenarios from setting a room temperature setting to predicting a component failure of an aircraft. Compared with developing an application or deploying a cloud service, is an IoT solution unique? If so, how? How does a typical IoT solution architecture consist? And what are the essential components and how are they relevant to each other? How does the security play out? What are the best practices in formulating...
SYS-CON Events announced today that Elastifile will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 - Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Elastifile Cloud File System (ECFS) is software-defined data infrastructure designed for seamless and efficient management of dynamic workloads across heterogeneous environments. Elastifile provides the architecture needed to optimize your hybrid cloud environment, by facilitating efficient...
In his session at @ThingsExpo, Arvind Radhakrishnen discussed how IoT offers new business models in banking and financial services organizations with the capability to revolutionize products, payments, channels, business processes and asset management built on strong architectural foundation. The following topics were covered: How IoT stands to impact various business parameters including customer experience, cost and risk management within BFS organizations.
SYS-CON Events announced today that Golden Gate University will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Since 1901, non-profit Golden Gate University (GGU) has been helping adults achieve their professional goals by providing high quality, practice-based undergraduate and graduate educational programs in law, taxation, business and related professions. Many of its courses are taug...
SYS-CON Events announced today that DXWorldExpo has been named “Global Sponsor” of SYS-CON's 21st International Cloud Expo, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Digital Transformation is the key issue driving the global enterprise IT business. Digital Transformation is most prominent among Global 2000 enterprises and government institutions.
An increasing number of companies are creating products that combine data with analytical capabilities. Running interactive queries on Big Data requires complex architectures to store and query data effectively, typically involving data streams, an choosing efficient file format/database and multiple independent systems that are tied together through custom-engineered pipelines. In his session at @BigDataExpo at @ThingsExpo, Tomer Levi, a senior software engineer at Intel’s Advanced Analytics ...
21st International Cloud Expo, taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, will feature technical sessions from a rock star conference faculty and the leading industry players in the world. 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. Me...
When shopping for a new data processing platform for IoT solutions, many development teams want to be able to test-drive options before making a choice. Yet when evaluating an IoT solution, it’s simply not feasible to do so at scale with physical devices. Building a sensor simulator is the next best choice; however, generating a realistic simulation at very high TPS with ease of configurability is a formidable challenge. When dealing with multiple application or transport protocols, you would be...
There is only one world-class Cloud event on earth, and that is Cloud Expo – which returns to Silicon Valley for the 21st Cloud Expo at the Santa Clara Convention Center, October 31 - November 2, 2017. 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. Companies are each developing their unique mix of cloud technologies and service...
WebRTC is great technology to build your own communication tools. It will be even more exciting experience it with advanced devices, such as a 360 Camera, 360 microphone, and a depth sensor camera. In his session at @ThingsExpo, Masashi Ganeko, a manager at INFOCOM Corporation, will introduce two experimental projects from his team and what they learned from them. "Shotoku Tamago" uses the robot audition software HARK to track speakers in 360 video of a remote party. "Virtual Teleport" uses a...
SYS-CON Events announced today that Secure Channels, a cybersecurity firm, will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Secure Channels, Inc. offers several products and solutions to its many clients, helping them protect critical data from being compromised and access to computer networks from the unauthorized. The company develops comprehensive data encryption security strategie...
Recently, WebRTC has a lot of eyes from market. The use cases of WebRTC are expanding - video chat, online education, online health care etc. Not only for human-to-human communication, but also IoT use cases such as machine to human use cases can be seen recently. One of the typical use-case is remote camera monitoring. With WebRTC, people can have interoperability and flexibility for deploying monitoring service. However, the benefit of WebRTC for IoT is not only its convenience and interopera...
When shopping for a new data processing platform for IoT solutions, many development teams want to be able to test-drive options before making a choice. Yet when evaluating an IoT solution, it’s simply not feasible to do so at scale with physical devices. Building a sensor simulator is the next best choice; however, generating a realistic simulation at very high TPS with ease of configurability is a formidable challenge. When dealing with multiple application or transport protocols, you would be...
SYS-CON Events announced today that App2Cloud will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct. 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. App2Cloud is an online Platform, specializing in migrating legacy applications to any Cloud Providers (AWS, Azure, Google Cloud).
IoT is at the core or many Digital Transformation initiatives with the goal of re-inventing a company's business model. We all agree that collecting relevant IoT data will result in massive amounts of data needing to be stored. However, with the rapid development of IoT devices and ongoing business model transformation, we are not able to predict the volume and growth of IoT data. And with the lack of IoT history, traditional methods of IT and infrastructure planning based on the past do not app...
Internet-of-Things discussions can end up either going down the consumer gadget rabbit hole or focused on the sort of data logging that industrial manufacturers have been doing forever. However, in fact, companies today are already using IoT data both to optimize their operational technology and to improve the experience of customer interactions in novel ways. In his session at @ThingsExpo, Gordon Haff, Red Hat Technology Evangelist, shared examples from a wide range of industries – including en...