Remove 2016 Remove Experimentation Remove Machine Learning Remove Modeling
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Enterprise Data Science Workflows with AMPs and Streamlit

Cloudera

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. We believe the best way to learn what a technology is capable of is to build things with it.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

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HPE Looks to Edge-to-Cloud Strategy for Growth in 2018/2019

Hurwitz & Associates

HPE then shed its software business, selling it to MicroFocus in 2016, and its EDS services business, selling it to CSC that same year. The $4 billion investment will be used for R&D, product development, technical services and the development of new consumption models for Edge and cloud. Consumption models are changing.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. In the present chapter [excerpt], we cover code that will enable you to create your own word vectors as well as to provide them as an input into a deep learning model. A complementary Domino project is available.

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Topics to watch at the Strata Data Conference in New York 2019

O'Reilly on Data

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. An ML-related topic, “models,” was No.

IoT 20
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AWS: Moving Beyond Infrastructure to Monetize its Ecosystem

Hurwitz & Associates

I would divide the announcements (too many to list) into four buckets: Alexa for Business; enterprise expansion; support for Kubernetes, and AI/machine learning Tools. Machine Learning and AI take center stage. In 2016 Amazon announced that the Amazon AI platform as a way to bring AI tools to its developer community.

IT 56
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When models are everywhere

O'Reilly on Data

Not all models are created equal, however: they operate on different principles, and impact us as individuals and communities in different ways. To understand the menagerie of models that are fundamentally altering our individual and shared realities, we need to build a typology, a classification of their effects and impacts.

Modeling 190