|By Marketwired .||
|September 3, 2014 01:00 PM EDT||
LANCASTER, NY -- (Marketwired) -- 09/03/14 -- Ecology and Environment, Inc. (E & E) (NASDAQ: EEI) has been awarded a two-year contract extension by the U.S. Army Corps of Engineers (USACE) Kansas City District to provide Architect-Engineer Services on a wide variety of Hazardous, Toxic and Radioactive Waste environmental projects. The award extends the current contract, valued at $21.4 million, through August 31, 2016. E & E has completed tasks totaling more than $9 million since the contract period began on September 1, 2011.
A majority of the work on the Indefinite Delivery Contract is related to Superfund projects in EPA Region 2, which includes New Jersey, New York, Puerto Rico, the U.S. Virgin Islands, and eight tribal nations. The scope of work includes site investigations, hazard evaluations, engineering studies, archaeological investigations, wetlands studies, groundwater modeling, contaminant fate and transport analysis, remedial designs, community relations, construction-phase services, evaluations of green technologies and energy conservation measures, and other related services.
E & E has worked on 565 projects under 60 contracts valued at over $200 million for the Army Corps of Engineers over a period of more than 30 years.
Headquartered in Lancaster, N.Y., E & E has completed thousands of projects for a wide variety of clients, including some of the most iconic, high-profile projects in the world. E & E is listed on the NASDAQ under the symbol EEI, and the information contained in this press release is available on the Company's website at www.ene.com.
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So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, provided tips on how to be successful in large scale machine learning...
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