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PG Madhavan
  • Bellevue, WA
  • United States
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Latest Activity

Tauheedul Ali liked PG Madhavan's blog post NEXT Machine Learning Paradigm: “DYNAMICAL"​ ML
Oct 26
PG Madhavan posted a status
"Just published this yesterday in MEDIUM – "Trouble with Learning . . . consequences for AI Business""
Jul 5
PG Madhavan is now friends with Mehul Nayak, Kleanthis Koupidis and Jane Brewer
Jul 3
PG Madhavan posted a blog post

AI Business Transformation Playbook for Executives

I am delighted to present my new blog - AI Business Transformation Playbook for Executives. originally posted here. I get into the nuts-and-bolts of AI Systems Solutioning in this rather lengthy blog but the “First Ten Plays” at the end summarizes the key steps. I look forward to your thoughts and comments.- PG“AI, IoT & 5G” – the confluence is a “perfect…See More
Jun 23
PG Madhavan's blog post was featured

AI Business Transformation Playbook for Executives

I am delighted to present my new blog - AI Business Transformation Playbook for Executives. originally posted here. I get into the nuts-and-bolts of AI Systems Solutioning in this rather lengthy blog but the “First Ten Plays” at the end summarizes the key steps. I look forward to your thoughts and comments.- PG“AI, IoT & 5G” – the confluence is a “perfect…See More
Jun 23
Sumudu Tennakoon liked PG Madhavan's blog post NEXT Machine Learning Paradigm: “DYNAMICAL"​ ML
Jun 2
PG Madhavan commented on William Vorhies's blog post Causality – The Next Most Important Thing in AI/ML
"Bill, yes, Causality Analysis IS the next wave in Data Science. Why? For BUSIINESS applications, dashboards, etc., are nice but the CxO wants to know which knobs to turn for a desired business outcome! Which is Prescriptive Analytics . . . not to…"
Apr 26
PG Madhavan liked William Vorhies's blog post Causality – The Next Most Important Thing in AI/ML
Apr 26

Profile Information

Short Bio
PG is an expert in Predictive Analytics. He has 20+ years of software products, platforms and framework experience in leadership roles at major corporations such as Microsoft, Lucent, AT&T and Rockwell and startups, Zaplah Corp (Founder and CEO) and Solavei.
http://www.jininnovation.com/index.html
My Web Site Or LinkedIn Profile
http://www.jininnovation.com/index.html
Professional Status
C-Level
Years of Experience:
25+ years
Your Company:
JIN Innovation
Industry:
Analytics
Your Job Title:
Algorist
How did you find out about DataScienceCentral?
LinkedIn
Interests:
Networking, New venture

PG Madhavan's Blog

AI Business Transformation Playbook for Executives

Posted on June 21, 2019 at 2:30pm 0 Comments

I am delighted to present my new blog - AI Business Transformation Playbook for Executives. originally posted here. I get into the nuts-and-bolts of AI Systems Solutioning in this rather lengthy blog but the “First Ten Plays” at the end summarizes the key steps. I look forward to your thoughts and…

Continue

IoT Data Science & “DML” – match made in heaven?

Posted on September 11, 2017 at 12:30pm 0 Comments

DML stands for “Dynamical Machine Learning” (more in the book, “SYSTEMS Analytics for IoT Data Science”, 2017). This match is not surprising once you realize that DML & IoT are both based on the venerable Systems Theory. Let us dig deeper . . .

Consider IoT for industrial applications. A machine is instrumented with sensors, data are collected in real-time (or at intervals), communicated to the cloud where IoT Data Science…

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History as a guide to IoT growth trajectory

Posted on April 2, 2017 at 1:00pm 1 Comment

Internet of Things (IoT) has generated a ton of excitement and furious activity. However, I sense some discomfort and even dread in the IoT ecosystem about the future – typical when a field is not growing at a hockey-stick pace . . .

“History may not repeat itself but it rhymes”, Mark Twain may have said. What history does IoT rhyme with?

 I have often used this diagram to crisply define IoT.…

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NEXT Machine Learning Paradigm: “DYNAMICAL"​ ML

Posted on March 18, 2017 at 2:30pm 1 Comment

Dynamical ML is machine learning that can adapt to variations over time; it requires “real-time recursive” learning algorithms and time-varying data models such as the ones described in the blog,…

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