Amazon discover.bot

Posted: November 1, 2019 in Clients, Projects, Uncategorized
Tags: , ,

I recently got a new gig as Industry Analyst for Amazon’s new discover.bot business unit. I will be advising on AI chatbot technology and writing periodic articles assessing how bots are quickly evolving across a broad variety of industries. My first piece is now available as of October 30, 2019, “Should a Chatbot Reveal Itself?” I’m looking forward to following this dynamic and growing industry segment.

I’m really excited to be teaching again! Starting April 2 and 4, 2019, I’m teaching two sections of the popular “Introduction to Data Science” course for UCLA Extension. In a previous life I taught at UNEX for 15 years, but after getting too busy as a data scientist, I had to walk away from teaching for a while. I’m happy to be returning to education now. I feel the time is right to get future data scientists up to speed to fill in the widening skills gap due to the accelerating rise in demand for data science professionals. Glad to be giving back!

[UPDATE: I’m teaching again Fall Quarter 2019 with another great group of newbie data scientists. My course GitHub repo is found HERE.]

 

Universal Standard Housing

Posted: January 28, 2019 in Clients, Projects

I’m pleased to announced that I have secured a new consulting relationship with Los Angeles based Universal Standard Housing (USH), a leader and innovator in the affordable housing industry. I will be providing data science consulting services to help lead an exciting new development project. This is a great start-up company, with some very smart and dynamic people. I’m looking forward to a successful deployment of machine learning based real estate solutions.

Open Data Science

Posted: August 30, 2018 in Clients, News, Projects, Uncategorized

I would like to announce that I’m now working with a new up-and-comer in the industry – Open Data Science, most commonly known for their Open Data Science Conferences (ODSC) West, India, Europe, and East. More recently, the company is providing a wealth of technical resources such as articles dealing with data science tools, modeling (machine learning), AI, deep learning, data viz, data transformation, academic research, and so much more. I will be making regular contributions as a data scientist. My first contribution is “Tips for Linear Regression Diagnostics.”

[UPDATE 1/9/2019] : since my original announcement, I’ve made 32 contributions for the ODSC blog. I just received excellent feedback from the company that my pieces were the most popular!

I’d like to announce my new educational resource – a complete video series of instructional screencasts designed to get you up to speed with data science and machine learning using the R statistical environment. The materials are offered over on O’Reilly Safari Books Online as “Data Science with R Master Class.”

This video series will show you how to apply R to your data science projects. You will learn to perform the data science process including data acquisition, data transformation, exploratory data analysis, data visualization, and statistical learning algorithm usage. This course will provide you with the tools and techniques required to excel with statistical learning methods in tackling important data problem domains. The R statistical environment was chosen for use in this course because many data scientists use it exclusively for their project work. All of the code examples for the course are written in R. In addition, many popular R packages and data sets will be used. The R code for all modules of the class can be found in this GitHub repo.

I’ve patterned the materials to be consumed in parallel with my book “Data Science and Machine Learning: An Introduction to Statistical Learning Methods with R.”

I was contracted by the FICO product marketing department to review their long term use of artificial intelligence (AI) for fraud, cybersecurity and compliance (AML) solutions. All three of these areas utilize machine learning and AI for anomaly detection. The flagship of this portfolio is the Falcon Fraud Platform, and is used by ~ 10,000 financial institutions to risk score approximately 9,000 payment card transactions/sec globally. I had a blast digging into all that FICO was doing with AI, and I came up with a summary document “5 Keys to Successfully Applying Machine Learning and AI in Enterprise Fraud Detection.” Here is a list of the 5 elements I focused on for the project:

  • The role of supervised and unsupervised models in fraud detection (with a focus on behavior anomaly analytics)
  • The importance of large data sets in model development and training
  • What are predicting features and why is domain expertise necessary in their development
  • The benefits of Specialized vs. Generic models in enterprise fraud (i.e., importance of expert features)
  • The role of adaptive analytics and/or self-learning AI in enterprise fraud

You can download the report here: 5_Keys_Successfully_Applying_Machine_Learning_ AI

I was very pleased to attend the GPU Technology Conference 2017 as the guest of host company NVIDIA on May 8-11 in Silicon Valley. This was my second GTC as I became acquainted with the GPU (graphics processing unit) universe last year while attending the conference. You can read my 2016 field report HERE. I was so impressed with NVIDIA last year, I assumed it was just an outlier and that this year the company would come back down to earth. I was wrong. I was equally impressed with what I saw at this year’s installment terms of how GPUs and NVIDIA are transforming the field of AI and deep learning. This Field Report chronicles what I saw and I’m delighted to share my experience!