Monday, September 18, 2017

IBM IoT - Products and Systems

Download the August 2017 Report by Aberdeen on IoT and Analytics: Better Manufacturing Decisions in the Era of Industry 4.0


Uploaded 19 September 2017


5 Sep 2017


25 August 2017


15 August 2017

Cognitive IoT is the use of cognitive computing technologies in combination with data generated by connected devices and the actions those devices can perform.

Get started with Watson Analytics

3 June 2016  uploaded



IBM wants to replace the spreadsheet with Watson Analytics

IBM Bluemix

What is Bluemix
The cloud platform powered by the world’s most popular open source projects

Products on Bluemix

Data and analytics
Application services
Internet of Things

Bluemix and Internet of Things

Experience a fully managed, cloud-hosted service designed to simplify and derive value from your IoT devices. See how companies are using Watson Internet of Things to transform their business from the inside out.
How it all fits together
Connect your device, send data to our cloud, set up and manage your devices, and use APIs to connect apps to your device data.

Start with your device – whether it’s a sensor, gateway, or something else – and let us help you connect it with one of our recipes.

Your device data is always secure when you connect to the cloud using open, lightweight MQTT messaging protocol or HTTP.
IBM Watson IoT Platform

The hub of the IBM IoT approach – set up and manage your connected devices so your apps can access live and historical data.

REST and real-time APIs
Use our secure APIs to connect your apps with data from your devices.

Your application and analytics
Create applications within IBM Bluemix, another cloud, or your own servers to interpret data.

How much will it (IBM Watson IoT Platform)  cost?

The IBM Watson IoT Platform charges on three metrics.

The average number of devices you connect over the month. You get 20 free devices a month with each plan.

The amount of data that devices exchange with the IBM Watson IoT Platform and associated applications.
You get free 100 MB data traffic a month with each plan (equivalent to 50,000 messages)
*assuming an average message size of 200 bytes

The amount of data stored in a historical database.
You get 1 GB free storage a month with each plan

Updated 20 September 2017,  20 September 2016,  24 March 2016

Monday, August 21, 2017

The Evolution of Internet of Things (IoT) - Developments in IoT

IoT Evolution Expo Las Vegas 2017

August 16, 2017


IoT Evolution

Cisco Keynote at IoT Evolution Expo Ft Lauderdale 2017
14 Mar 2017
Maciej Kranz of Cisco speaks at IoT Evolution Expo Ft Lauderdale 2017


Cisco Live 2016: The Business of IoT: Go Fast and Grow Fast Now
13 Jul 2016
The Business of IoT: Go Fast and Grow Fast Now - Rowan Trollope, Jahangir Mohammed, and Sandy Hogan.




A white paper on IoT evolution by Texas Instruments

Monday, July 31, 2017

Recent Books - IoT - Industry 4.0

E-Technologies: Embracing the Internet of Things: 7th International Conference, MCETECH 2017, Ottawa, ON, Canada, May 17-19, 2017, Proceedings

Esma Aïmeur, Umar Ruhi, Michael Weiss
Springer, 09-Jun-2017 - Computers - 319 pages

This book constitutes the refereed proceedings of the 7th International Conference on E-Technologies, MCETECH 2017, held in Ottawa, ON, Canada, in May 2017.
This year’s conference drew special attention to the ever-increasing role of the Internet of Things (IoT); and the contributions span a variety of application domains such as e-Commerce, e-Health, e-Learning, and e-Justice, comprising research from models and architectures, methodology proposals, prototype implementations, and empirical validation of theoretical models.

The 19 papers presented were carefully reviewed and selected from 48 submissions. They were organized in topical sections named: pervasive computing and smart applications; security, privacy and trust; process modeling and adaptation; data analytics and machine learning; and e-health and e-commerce.

Industry X.0: Realizing Digital Value in Industrial Sectors

Eric Schaeffer
Kogan Page Publishers, 03-May-2017 - Business & Economics - 192 pages

Industry X.0 takes an insightful look at the business impact of the Internet of Things movement on the industrial sphere. Eric Schaeffer combines deep analysis with practical strategic guidance, and offers tangible and actionable recommendations on how to realise value in the current digital age. Based on extensive research and insights into the six core competencies that have been identified by Accenture, Industry X.0 explores critical aspects of the Industrial Internet of Things (IIoT), discussing and defining them in an engaging and accessible manner. These include managing smart data, handling digital product development, skilling up the workforce, mastering innovation, making the most of platforms and ecosystems, and much more.

Meticulously researched and clearly explained, Industry X.0 makes a stringent case for companies to actively shift mind-sets away from products, towards services, value and outcomes. Complemented by a wealth of case studies and real world examples, this book provides invaluable, practical 'how-to' advice for business organizations as they embark on their journeys into the era of the IIoT.

Thursday, July 27, 2017

AI, Machine Learning & Deep Learning - Education - Training Programs - USA

Technical Introduction to AI, Machine Learning & Deep Learning
Engineered Education
Friday, July 28, 2017 from 9:00 AM to 7:00 PM (PDT)
San Francisco, CA
Registration $495.00 $13.37
Team Discount (4 or more registrations)   $349.00 $9.72

This workshop will arm you with the tools to get started using machine learning in your day job and the resources to find additional help if you want to go deeper.
The course is expertly designed to leave you with the ability to take training data, do feature selection and actually build models for applications like content categorization, sentiment analysis, and image recognition. By the end of the day, students will be able to use models in their day-to-day work. You will also walk away with a high-level understanding of how common models such as Deep Neural Networks, SVMs, Logistic Regression and Naive Bayes work and when to use them.

Technologies Introduced

Intro to Machine Learning

Intro to Deep Learning

Intro to Machine Learning Platforms
Google Cloud ML
Azure ML
Amazon ML

We try to make this class as accessible as possible. Some proficiency with Python is necessary. If you can open up a Jupyter notebook and install requisite software that’s helpful but we’ll also cover how to do that quickly in the beginning.

What you Need to bring

You must also bring your own laptop (don’t forget your charger).

It saves a lot of time if you can get your laptop setup in advance.  If you can't get everything setup, try to come early and we'll help you with the installation.

Download code for the class from

There are instructions on this website for how to install all the necessary programs at - if you have questions, you can email us or put them in the github issues tracker where they might help another student.

Lukas Biewald:  Lukas Biewald is the founder of CrowdFlower, an Artificial Intelligence company that works with data science teams at Google, Bloomberg, Facebook and hundreds of other organizations to make machine learning work in the real world. Prior to that, Lukas was the first data scientist at Powerset (Acquired by Microsoft and rebranded as Bing) and a scientist at Yahoo!, Lukas was shipping machine learning algorithms to hundreds of millions of users.

Lukas frequently teaches invited Machine Learning workshops with Galvanize, O’Reilly and ODSC. He is a frequent contributor to Computerworld, Forbes and O’Reilly and has presented at the machine learning academic conferences such as AAAI, SIGIR, ACL and EMNLP. He was in Inc’s annual 30 under 30 and was also a finalist at TechCrunch Disrupt.

9:00 – 10:00 Breakfast and Intro to Machine Learning
We will assume no knowledge of Machine Learning, so we'll go over terminology and the history of Machine Learning and Artificial Intelligence.  We'll talk about the common use cases and how they fit in with the different Machine Learning algorithms.

10:00 – 12:00 Build a Sentiment Classifier From Scratch
Everyone builds a Twitter sentiment classifier using scikit-learn. We try multiple feature selection approaches and multiple model types. We learn some common tricks for actually making machine learning effective in the real world.

12:00-1:00 Lunch and Overview of State Machine Learning
Eat lunch and for your eating entertainment, Lukas will introduce a little math, stats and history of how machine learning got to where it is today.  We will go over the state of machine learning platforms today and how to get an entry-level job in machine learning for those that are interested.

1:00-2:30 Try the Common Machine Learning Platforms
These days, there are many excellent, scalable, low cost machine learning platforms. We will try rebuilding our sentiment classifier on two of the most common: Microsoft Azure ML and Amazon ML.

2:30-3:00 Break and Q&A
We can discuss other applications of this technology and look at how it might apply to real-world tasks that students may be working on.

3:00-5:00 Introduction to TensorFlow and Deep Neural Networks
We will learn how deep neural networks work and actually build one! If you bring a laptop with a GPU that supports CUDA (for example a MacBook with Mac OS X 10.11 or later), we’ll see if we can make it GPU accelerated.
We’ll all build a network to do handwritten digit recognition.

5:00-5:30 Wrap-up and Q&A
We will finish up and discuss how to apply this knowledge directly to problems that we actually face in our jobs.

5:30-7:00 Drinks & Networking
We’ll bring together top entrepreneurs, tech executives & engineers to connect with and learn from. Plus, this is a chance to meet your classmates and teachers in an informal and fun setting.

Sunday, July 23, 2017

Data Mining - Mining of Massive Data Sets

Standford Course Page of Mining of Massive Data Sets

You can download full book published by Cambridge Press

Jure Leskovec, Anand Rajaraman, Jeff Ullman

Thursday, July 20, 2017

Data Mining - Data Analysis - Credit Scoring

Asurvey of applying machine learning techniques existing models and open issues
Neural information processing

Monday, July 17, 2017

Computer Science - Information Board

MIT’s Daniela Rus is leading a robotics revolution
Posted Jul 11, 2017 by Brian Heater (@bheater)

Saturday, July 15, 2017

Adoption of Cloud Computing


According to IDC, spending on public cloud computing alone will likely increase 24.4 percent in 2017 to reach $122.5 billion. And the same firm forecasts that spending on private cloud infrastructure will grow 16.6 percent this year.

For the fourth quarter of 2016, the survey found that the average organization uses 1, 427 different cloud services,

Cloud Adoption Is Growing But Forecasts Differ on How Much
Barb Darrow
Feb 22, 2017
Overall demand for cloud computing in all its forms will grow 18% this year to $246.8 billion in total worldwide revenue from $209.2 billion, according to a new forecast from market research firm Gartner.

The subset of services called public cloud infrastructure is expected to grow at 36.8% this year to $34.6 billion in revenue worldwide.
Software-as-a-Service (SaaS),  should grow about 20% to $46.3 billion.

IDC estimated that overall cloud services will grow 24.4% year over year to $122.5 billion with SaaS accounting for  60% of total.

Leaders and laggards in enterprise cloud infrastructure adoption
By Nagendra Bommadevara, James Kaplan, and Irina Starikova
October 2016

To address those frustrations, between 2014 and 2016 we surveyed senior business and technology leaders in more than 50 large organizations (Most of them in Fortune 100 in Europe and North America to find out about their adoption of cloud and next-generation infrastructure.1

Despite their high-priority, highly visible, multiyear efforts to implement cloud programs, half of the participants in our survey say they have moved no more than 5 percent of their x86 processing workloads to cloud environments (private or public).

Enterprise Cloud Services


Consumer Cloud Services

Google Drive

Saturday, June 24, 2017

Sunday, June 18, 2017

Microsoft Power BI

What is Power BI?

Power BI is a suite of business analytics tools that deliver insights throughout your organization. Connect to hundreds of data sources, simplify data prep, and drive ad hoc analysis. Produce beautiful reports, then publish them for your organization to consume on the web and across mobile devices. Everyone can create personalized dashboards with a unique, 360-degree view of their business. And scale across the enterprise, with governance and security built-in.

The rise of self service BI


Microsoft Power BI

Thursday, June 8, 2017

Cloudera Altus - Data Engineering Solution

A cloud service for engineers

For data engineers
Focus on the workload. Cloudera Altus elevates data pipeline operations over cluster operations

Leave the management to Altus. Data engineering experience is delivered as a service. Altus takes care of cluster management and operations

Analyze and troubleshoot jobs. Don’t waste time sifting through logs to find the root cause of a failed job. Monitor your work as it’s running, or let us help you troubleshoot

Simple migration and backward compatibility. Easily move your on-prem workloads to and from the cloud with minimal risk. Leverage new platform versions without breaking compatibility with existing applications

Data engineering made easy
Cloudera Altus is a managed service that makes it easier than ever to execute data pipelines. Launch your cluster in minutes on AWS, and start exploring and extracting value from all your data. Over time Cloudera plans to expand Altus to support other leading public clouds such as Microsoft Azure, etc.

The initial rollout of Cloudera Altus includes support for Apache Spark, Apache Hive on MapReduce2, and Hive on Spark. It is available today in most Amazon Web Services (AWS) regions.

Wednesday, June 7, 2017

Google BIGQUERY - Enterprise Cloud Data Warehouse

BigQuery is Google's fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don't need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. BigQuery is a powerful Big Data analytics platform used by all types of organizations, from startups to Fortune 500 companies.

Speed & Scale

BigQuery can scan TB in seconds and PB in minutes. Load your data from Google Cloud Storage or Google Cloud Datastore, or stream it into BigQuery to enable real-time analysis of your data. With BigQuery you can easily scale your database from GBs to PBs.

Building and scaling new business models to gain insights from disparate data faster, while reducing IT costs, requires an architecture that can go from prototype to petabyte scale as your needs evolve. Google BigQuery’s serverless architecture can help ensure that your enterprise data warehouse withstands growth at any scale. Informatica helps you unlock the power of hybrid data with high performance, highly scalable data management solutions that efficiently move and manage large volumes of data to Google BigQuery. Informatica and Google BigQuery is the best combination for modernizing your data architecture.

Monday, June 5, 2017

Blockchain Technology - Introduction and Bibliography

What is Blockchain Technology?

“The blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value.”
Don & Alex Tapscott, authors Blockchain Revolution (2016)

Alex Tapscott: "Blockchain Revolution" | Talks at Google
11 July 2016

Talks at Google

Dan Tapscott

What is Blockchain Technology? A Step-by-Step Guide For Beginners
An in-depth guide by BlockGeeks