Archive for the ‘Clients’ Category

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 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

insidebigdata_guide_dl_aiI’m very pleased to announce the availability of a new technology guide that I was contracted to research, develop and write – “insideBIGDATA Guide to Artificial Intelligence & Deep Learning” sponsored by NVIDIA.

This guide to artificial intelligence explains the difference between AI, machine learning and deep learning, and examines the intersection of AI and HPC. The guide includes a special section highlighting the results of a new insideBIGDATA audience survey to get readers thoughts about how they see AI, machine learning and deep learning for their own companies. The guide provides some of the survey results including numeric results, data visualization, and interpretation of the results.

You can download a copy of the guide HERE.

Opera-Solutions-LogoI recently was awarded an extended contract with Opera Solutions to continue as an industry analyst and contributor for their blog SignalCentral: The Hottest Spot for Big Data Science. I will be writing on a broad range of topics including big data, data science, machine learning, AI, and deep learning. Opera Solutions is a global provider of advanced analytics software solutions that address the persistent problem of scaling Big Data analytics. The company has over 175 data scientists on staff. My original announcement was on March 31, 2014. I’m very pleased to continue my work with this leading consulting group.

Here is an on-going list of blog post written on behalf of Opera Solutions:

7 Steps to Prepare for Data Science Adoption

AI, Machine Learning, and Deep Learning Explained

Ensuring Predictive Analytics Success with Data Preparation & Quality

AI: Why Now? An Old Technology Grows Up Fast