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

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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!

I’m very pleased to announce the availability of a new special technology report that I was contracted to research, develop and write – “insideHPC Special Report: Riding the Wave of Machine Learning and Deep Learning” sponsored by Dell EMC and NVIDIA. insideHPC is the sister publication (specializing in high performance computing) to insideBIGDATA where I serve as Managing Editor.

The report focuses on how many companies are moving decisively to develop capabilities based on AI, machine learning and deep learning. In time-honored business fashion, the motivation is a combination of fear and hope. Competitive pressures are spurring companies on, and there is a sense of urgency among many enterprise thought leaders about not falling behind.

You can download a copy of the report HERE.

I was pleased to be invited as an advisor for a full-day UCLA Career Development Conference for Graduate Students and Postdocs on May 4, 2017 on the beautiful UCLA campus. My topic of expertise: Data Science. It appears that many newly graduated Masters and Ph.D. students, as well as post doctoral researchers are interested in making the transition to the field of data science. During the round table discussions, I had the opportunity to meet with and advise many bright young minds, all of whom would make excellent data scientists. They came from a diverse set of academic disciplines including statistics, neurosciences, engineering, bio statistics, psychology just to name a few. I truly enjoy these opportunities to share my experiences in the field which I believe is the best profession around!

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.

UCLA Young Alumni Career Forum

Posted: January 19, 2017 in Events

ucla_career_centerI will be heading up the Big Data/Data Science area for the upcoming UCLA Young Alumni Career Forum on Saturday, Jan. 21 on the UCLA campus in Los Angeles. The forum presents a unique opportunity for recent UCLA grads with a focus on career strategies where professional career counselors, employers, and nationally recognized speakers will share effective strategies to strengthen skills as the candidates advance their early careers.

I’m eager to advise future data scientists all about the tremendous opportunities in this field. I feel the timing is excellent to transition into data science, and big data engineering. This is my opportunity to help shape new careers and help fellow UCLA alumni along the way. Looking forward to it!

ucla_alumni_chat

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