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NVIDIA RAPIDS in Cloudera Machine Learning

Cloudera

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. RAPIDS on the Cloudera Data Platform comes pre-configured with all the necessary libraries and dependencies to bring the power of RAPIDS to your projects. Register Now. .

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The top 15 big data and data analytics certifications

CIO

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. Organization: Columbia University Price: Students pay Columbia Engineering’s rate of tuition (US$2,362 per credit).

Big Data 190
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CDP Data Visualization: Self-Service Data Visualization For The Full Data Lifecycle

Cloudera

From our release of advanced production machine learning features in Cloudera Machine Learning, to releasing CDP Data Engineering for accelerating data pipeline curation and automation; our mission has been to constantly innovate at the leading edge of enterprise data and analytics.

Data 111
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Enabling The Full ML Lifecycle For Scaling AI Use Cases

Cloudera

When it comes to machine learning (ML) in the enterprise, there are many misconceptions about what it actually takes to effectively employ machine learning models and scale AI use cases. Accelerating the Full Machine Learning Lifecycle With Cloudera Data Platform. Laurence Goasduff, Gartner.

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Apiumhub as Partner of the Data Innovation Summit 2023 

Apiumhub

In this event, hundreds of innovative minds, enterprise practitioners, technology providers, startup founders, and innovators come together to discuss ideas on data science, big data, ML, AI, data management, data engineering, IoT, and analytics.

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Change The Way You Do ML With Applied ML Prototypes

Cloudera

Cloudera has a front-row seat to organizational challenges as those enterprises make Machine Learning a core part of their strategies and businesses. The work of a machine learning model developer is highly complex. We work with the largest companies in the world to help tackle their most challenging ML problems.

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Five Trends for 2019

Hu's Place - HitachiVantara

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Building an AI or machine learning model is not a one-time effort.

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