Keith Gregory talks to Andrew Ganim, one of Chariot’s experienced software consultants, about his recent project: building a data pipeline for a multinational company.
Check out our YouTube playlist to watch all the talks from Emerging Technologies for the Enterprise 2020. Abstract As massive amounts of new geospatial data are collected, it is increasingly challenging to search and find data of interest. New upcoming NASA missions, such as NISAR and SWOT will be generating tens of terabytes a day, … Read More
Check out our YouTube playlist to watch all the talks from Emerging Technologies for the Enterprise 2020. Abstract Parkinson’s disease (PD) is a chronic, degenerative neurological disorder that affects as many as one in a 100 people over the age of 60. It is estimated that more than 8 million individuals have PD worldwide, and … Read More
Gathering, cleaning, manipulating, and assessing data is a complex (and expensive) job – especially if the data takes a wide variety of forms, and comes from many different sources. So why should companies invest in that work?
This talk will review two common use cases for the use of captured metric data: 1) Real-time analysis, visualization, and quality assurance, and 2) Ad-hoc analysis. The most common open source streaming options will be mentioned, however this talk be concerned with Apache Flink specifically. A brief discussion of Apache Beam will also be included in the context of the larger discussion of a unified data processing model.
In this episode, host Ken Rimple talks to Chris Baglieri of BlackFynn on the company’s work in health research-related data science. He discusses how he and his team aids researchers who are attacking Parkinson’s disease and other disorders. They’ve been doing the deep genetic research that has benefitted cancer research over the past two decades. … Read More
Disclaimer: Some basic exposure to machine learning is assumed. Neural nets are on the rise, now that computing power and parallel data processing capabilities have reached the levels that allow them to shine. Recurrent neural nets, the more sophisticated kind that possess time dynamics, have achieved spectacular results in certain areas. Overfitting, however, has … Read More
I recently attended the O’Reilly AI Conference in New York where artificial intelligence practitioners showcased the impressive strides they’ve made so far in using AI for real-world applications
Predictive modeling is one of the figureheads of big data. Machine Learning Theory asserts that the more data the better, and empirical observations suggest that the more granular data, the better the performance (provided you have modern algorithms and big data) but the paradox of predictive modeling is that when you need models the most, even all the data is not enough.
Tracey Welson-Rossman talks to Anita Garamella Andrews, VP of Client Analytics Services at R.J. Metrics, about analytics and actionable data.