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Tag: Data Scientists

BI Leader Sisense Acquires Periscope Data

May 14, 2019 |

Sisense’s acquisition of Periscope Data combines the buyer’s business intelligence platform with Periscope’s cloud data analytics expertise as Sisense looks to up its analytics game.

The merger announced on Tuesday (May 14) gives New York-based Sisense access to Periscope Data’s analytics platform as it seeks to upgrade its current business intelligence platform to meet the needs of data scientists. Read more…

AI Hype Rockets, Hadoop Twins, and Other Learnings from Strata

Mar 28, 2019 |

Strange similarities have been discovered to exist between Cloudera and Hortonworks now that the two former Hadoop rivals have merged into a single company. In fact, the resemblances are so great that they appear to be like “twins separated at birth,” Read more…

How To Find and Hire Data Scientists

Jan 23, 2019 |

So you’re building a data science team. That’s great news! As a business leader, finding a qualified data scientists is a critical step in your company’s ability to harness big data and machine learning technologies, which is a competitive advantage. Read more…

The Hard Questions of Hiring For Machine Learning

Nov 22, 2018 |

I’ve been thinking a lot about hiring for the machine learning specialization lately. It’s no surprise. New data is emerging almost daily about the rise of machine learning, artificial intelligence and deep learning in software design. Read more…

Empowering Citizen Data Science

Aug 13, 2018 |

Companies of all stripes are turning to data science to unlock the value in their data. However, finding highly trained data scientists to build the systems has proven to be a very difficult task. Read more…

Anaconda: Data Science Exiting Hadoop for the Cloud

Jun 14, 2018 |

Data scientists are embracing cloud-native frameworks as they move on from on-premises data infrastructure previously dominated by Hadoop, concludes a survey on the state of data science.

The shift is driven in part by the enterprise transition from merely managing big data to using machine learning and other connected data tools to glean insights in real time, according to the data science survey released this week by Python platform specialist Anaconda Inc. Read more…

Opening Up Black Boxes with Explainable AI

May 30, 2018 |

One of the biggest challenges with deep learning is explaining to customers and regulators how the models get their answers. In many cases, we simply don’t know how the models generated their answers, even if we’re very confident in the answers themselves. Read more…

Why Developers Need to Think Like Data Scientists

May 18, 2018 |

Data is growing faster than is even fathomable. By 2020, roughly 1.7 megabytes of new information will be created every second for every human being on the planet. Given the immense amount of data, it’s no wonder there is a call in all industries for talent that can collect and analyze the data. Read more…

What Kind of Data Scientist Are You?

Mar 15, 2018 |

If you’ve worked with the data science community, you’ve probably interacted with data scientists and formed a definition for the increasingly popular position. But it turns out, not all data scientists are alike, and according to a recent analysis by researchers at UCLA and Microsoft, there are actually nine different types of data scientists. Read more…

ParallelM Aims to Close the Gap in ML Operationalization

Feb 21, 2018 |

A startup named ParallelM today unveiled new software aimed at alleviating data scientists from the burden of manually deploying, monitoring, and managing machine learning pipelines in production.

Dubbed MLOps, ParallelM‘s software helps to automate many of the operational tasks required to turn a machine learning model from a promising piece of code running nn Spark, Flink, TensorFlow, or PyTorch processing engines into a secure, governed, and production-ready machine learning system. Read more…

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