Intelligent Site Reliability Engineering – A Machine Learning Perspective

“May the queries flow, and the pager stay silent. —Traditional SRE blessing” Like *.js, DevOps / SRE isn’t completely immune from the dilemma of choice, or confusion as a service as some would like to call. Should it be Nagios or Zabbix or Cacti or Zenoss or Borgmon? Regardless of which tool one decides for monitoring usage,...

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Exploring Spark with Data Science Work bench

Apache Spark is a general purpose cluster computing platform which extends map-reduce to support multiple computation types including but not limited to stream processing and interactive queries. Last week IBM's Moktar Kandil presented at the Tampa Hadoop and Tampa Data Science Group Joint meetup on the topic of exploring Apache Spark. Apache Spark for Azure HD-Insight Following are...

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State of Facial Recognition (Azure Face API et al) & Sentiment Analysis

Sam just wrote a precis on Why Facial Recognition Is the Next Big Thing in Marketing which outlines how brands are / can use the facial recognition to increase engagement, and therefore sales. From a machine learning and data science perspective, building algorithms which understand what one's face is really saying i.e. performing emotion analysis to find insight into purchasing patterns...

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The five Tribes of Machine Learning, and other algorithmic tales

Pedro Domingos' The Master Algorithm - How the Quest for the Ultimate Learning Machine Will Remake Our World is an interesting and thought provoking book about the state of machine learning, data science, and artificial intelligence.   Categorizing,  classifying and clearly representing the ideas around any rapidly developing/evolving field is hard job. Machine learning with its...

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On Explainability of Deep Neural Networks

During a discussion yesterday with software architect extraordinaire David Lazar regarding how everything old is new again, the topic of deep neural networks and its amazing success was brought up. Unless one is living under a rock for past five years, the advancements in artificial neural networks (ANN) has been quite significant and noteworthy. Since the...

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MIT Machine Learning for Big Data and Text Processing Class Notes Day 5

On the final day (day 5) the agenda for the MIT Machine learning course was as follows: Generative models, mixtures, EM algorithm Semi-supervised and active learning Tagging, information extraction The day started with Dr. Jakkola's discusion on parameter selection, generative learning algorithms,  Learning Generative Models via Discriminative Approaches, and Generative and Discriminative Models. This led to the questions such...

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