When one looks at the amazing roster of talks for most data science conferences what you don’t see is a lot of discussion on how to leverage object storage. On some level you would expect to — ultimately if you want to run your Spark or Presto job on peta-scale data sets and have it be available to your applications in the public or private cloud — this would be the logical storage architecture.
While logical, there has been a catch, at least historically, and that is object storage…Continue
Added by Jonathan Symonds on June 25, 2019 at 9:00am — No Comments
I recently downloaded a 5 year Public Use Microsample (PUMS) from the latest release of the American Community Survey (ACS) census data. The data contain a wealth of demographic information on both American households and…Continue
Added by steve miller on June 24, 2019 at 12:42pm — No Comments
Summary: Business doesn’t want AI. Business wants results. While we were focused inward on our Advanced Analytic Platforms, smart competitors were rolling up AI/ML with other capabilities into “Intelligent Automation” platforms. This large scale integration of capabilities of which AI/ML is only a part looks a lot like the development of ERPs in the late 90s.
Added by William Vorhies on June 24, 2019 at 8:23am — No Comments
The Catch 22 problem holding back AI application adoption ...
Last week, there was an interesting report in the MIT technology review that Artificial Intelligence can help construction industry to help see…
Added by ajit jaokar on June 24, 2019 at 12:30am — No Comments
Added by Vincent Granville on June 23, 2019 at 6:00pm — No Comments
This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new…Continue
Added by Vincent Granville on June 23, 2019 at 1:00pm — No Comments
Added by Stephanie Shen on June 23, 2019 at 7:30am — No Comments
I am delighted to present my new blog - AI Business Transformation Playbook for Executives. originally posted here. I get into the nuts-and-bolts of AI Systems Solutioning in this rather lengthy blog but the “First Ten Plays” at the end summarizes the key steps. I look forward to your thoughts and…Continue
Added by PG Madhavan on June 21, 2019 at 2:30pm — No Comments
Our client was a pioneering company in producing and delivering Roof Shingles. Their main plant in Minnesota and they have around 25 more plants across US. Client implemented 100’s of sensors along the assembly line that are streaming nano-second data to their Spark Data-lake.
Viscosity of input fluid is an important factor to maintain quality of production of roof shingles. Data shows there are unwanted peaks (outliers) in viscosity data which client wanted to eliminate. Following…Continue
Added by Dr. Moloy De on June 21, 2019 at 3:10am — No Comments
Bayesian Machine Learning (part - 1)
As a data scientist, I am curious about knowing different analytical processes from a probabilistic point of view. There are two most popular ways…Continue
Added by Ashutosh vyas on June 20, 2019 at 10:30pm — No Comments
Here is our selection of featured articles and technical resources posted since Monday:
Added by Vincent Granville on June 20, 2019 at 11:30am — No Comments
This article broadly describes the capabilities that constitute an enterprise analytics program or competency. The intention initially, was to provide tips on mitigating challenges encountered in implementing an analytics practice - but that is going to be relegated to a future article.
IT projects in general, and analytics projects, in particular, are notoriously unsuccessful or "challenged".
Focusing attention on the following short list prior to embarking on an analytics…Continue
Added by Sagren Pillai on June 20, 2019 at 2:00am — No Comments
In the beauty industry, chatbots are seen to solve much more than tangible problems. During the initial phase, its usage was straightforward and cautious - Personalized communication, 24/7 availability, product inquiry, and reaching the target audience.
But as the beauty world is much more personalized than any other industry, the big brands started leveraging chatbot in a lot more personal aspects.
Added by Amit Dua on June 20, 2019 at 1:30am — No Comments
Machine Learning has seen a tremendous rise in the last decade, and one of its sub-fields which has contributed largely to its growth is Deep Learning. The large volumes of data and the huge computation power that modern system possess has given Data Scientist, Machine Learning Engineers, and others to achieve ground-breaking results in the Deep Learning and continue to bring in new developments in this field.
In this blog post, we would cover the deep learning data sets that you…Continue
Added by Divya Singh on June 19, 2019 at 8:00pm — No Comments
The most frequent question I get about AI from colleagues, product managers and others, is,
"What do I need to know about AI and what's the best way to learn it?"
I've invested a considerable amount of time taking…Continue
Added by Mark Cramer on June 19, 2019 at 12:47pm — No Comments
In an earlier description of clustering algorithms we described an algorithm by which locally optimum partitions and center of gravity of multi-dimensional vectors/points may be obtained. If only one or two dimensional data are considered the optimum partitioning to obtain the so-called Voronoi regions are known. For one-dimension it is the interval while for two-dimensions it is hexagon (think of honey-bee nests or cellular…Continue
Added by Faramarz Azadegan on June 19, 2019 at 9:35am — No Comments
A myriad of options exist for classification. In general, there isn't a single "best" option for every situation. That said, three popular classification methods— Decision Trees, k-NN & Naive Bayes—can be tweaked for practically every situation.
Naive Bayes and K-NN, are both examples of supervised learning (where the…Continue
Added by Stephanie Glen on June 19, 2019 at 6:49am — No Comments
In the statistical literature, for ordinal types of data, are known lots of indicators to measure the degree of the polarization phenomenon. Typically, many of the widely used measures of distributional variability are defined as a function of a reference point, which in some “sense” could be considered representative for the entire population. This function indicates how much all the values differ from the point that is…Continue
Added by Ludovico Pinzari on June 17, 2019 at 11:54pm — No Comments
Summary: Forrester has just released its “New Wave™: Automation-Focused Machine Learning Solutions, Q2 2019” report on leading stand-alone automated machine learning platforms. This is our first good side-by-side comparison. You might also want to consider some who were not included.
The domestic economy gets directly and indirectly impacted by the Oil and Gas Industry. It is pretty much noticeable that the oil and gas prices directly affect the economy as a whole. If the United States is to be picked, oil and gas is crucial to its individuals and businesses. But, this does not cancel out the reality that the rest of the globe requires oil and gas in equal measures to run a productive life and business operations. This is the reason why the Oil and Gas Industry requires…Continue
Added by Sanjeev Verma on June 17, 2019 at 3:30am — No Comments