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Ransomware Attack Shuts Down Top U.S. Gasoline Pipeline

Slate

One of the biggest fuel pipeline operators in the United States shut down its entire network after a ransomware attack. The attack on Colonial Pipeline, which operates the biggest gasoline pipeline in the country, brought to center stage how critical infrastructure is facing increasing threats from hackers who are getting more sophisticated. Colonial, which carries almost half of the gasoline, diesel, and jet fuel for the East Coast and has a capacity of around 2.5 million barrels a day, has hired a cybersecurity firm to investigate what happened as it works to restore its operations. The company said it decided to take "certain systems offline to contain the threat, which has temporarily halted all pipeline operations, and affected some of our IT systems." "This was not a minor target," Amy Myers Jaffe, an energy expert, tells Politico.


Obama administration under fire for intervening in North Dakota pipeline case

FOX News

The Obama administration is coming under increasing pressure from lawmakers and oil industry groups to stand down after intervening in yet another pipeline dispute – this time, by temporarily suspending construction on the controversial North Dakota Access Pipeline. The Standing Rock Sioux Tribe has been fighting the pipeline for two years, arguing it could traverse sacred ground and burial sites and pose health problems. Their bid to block the four-state, 1,172-mile pipeline got a boost Friday from the administration, which temporarily halted the project just minutes after U.S. District Judge James Boasberg ruled against the tribe. The decision to put on hold the 3.8 billion project set off a firestorm among industry leaders and lawmakers who say the administration has overstepped its authority. Rep. Kevin Cramer, R-N.D., called the reversal "fundamentally unfair" and said it "does nothing to ensure certainty or calm, but rather adds further confusion."


Managing Spark and Kafka Pipelines - Silicon Valley Data Science

@machinelearnbot

Do you fully understand how your systems operate? As an engineer, there is a lot you can do to aid the person who is going to manage your application in the future. In a previous post we covered how exposing the tuning knobs of the underlying technologies to operations will go a long way to making your application successful. Your application is a unique project--it's easier for you to learn the operational aspects of the underlying technologies, than for others to learn the specifics of all the applications. Notice I said "the person who is going to manage your application in the future" and not "operations."


Machine Learning in Python - Extras

#artificialintelligence

Machine Learning applications are everywhere nowadays from Google Translate and NLP API,to Recommendation Systems used by YouTube,Netflix and Amazon,Udemy and more. As we have come to know, data science and machine learning is quite important to the success of any business and sector- so what does it take to build machine learning systems that works? In performing machine learning and data science projects, the normal workflow is that you have a problem you want to solve, hence you perform data collection,data preparation,feature engineering,model building and evaluation and then you deploy your model. However that is not all there is, there is a lot more to this life cycle. In this course we will be introducing to you some extra things that is not covered in most machine learning courses - such as working with pipelines specifically Scikit-learn pipelines, Spark Pipelines,etc and working with imbalanced dataset,etc We will also explore other ML frameworks beyond Scikit-learn,Tensorflow or Pytorch such as TuriCreate, Creme for online machine learning and more.


DevOps Pipeline for a Machine Learning Project 7wData

#artificialintelligence

Machine Learning is getting more and more popular in applications and software products, from accounting to hot dog recognition apps. When you add machine learning techniques to exciting projects, you need to be ready for a number of difficulties. The Statsbot team asked Boris Tvaroska to tell us how to prepare a DevOps pipeline for an ML based project. There is no shortage in tutorials and beginner training for data science. Most of them focus on "report" data science.