Modern Data Analytics in Telecom

Modern Data Analytics in Telecom
No industry relies more on data analytics for innovation than telecommunications. From increases in data volumes, the rise of 5G, and more and more connected devices, only the organizations that invest in a modern data infrastructure will be prepared to integrate, process, and analyze the mountains of new data. What are you doing today to get ready?
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Spotlight

OTHER ON-DEMAND WEBINARS

Applying Convolutional Neural Networks with TensorFlow

Databricks

In this latest Data Science Central Deep Learning Fundamentals Series webinar, we will cover the fundamentals behind TensorFlow and how to apply them within a convolutional neural network (CNN) example. The principles we will cover include CNN concepts and their impact to the accuracy and loss of your network. All these concepts will be brought to life by demonstrating how Databricks simplifies deep learning - letting you quickly access ready-to-use ML environments, as well as prepare data, and train models faster. After this session, if requested, you will receive the presentation and associated notebooks so you can run the samples yourself.
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Accelerating Project Delivery Fast & Data Compliant

Watch our webinar fast data delivery demo for a walkthrough, showing you how to leverage Accelario to increase DevOps velocity, while achieving data compliance and reducing storage costs.
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Overcoming Data Management Challenges in AI/ML

The ever-growing data landscape drives initiatives to automate many aspects of the analytics lifecycle; such as data access, enablement of semantics, BI and others. Automation has become an integral part of our daily lives in the enterprise data fabric. The AI-driven initiative to automate the data access and provide guidance to the right data assets, correlates with the initiatives of the data scientists to get access to more curated data.
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Enabling the Third Wave of Analytics: Embedded BI Takes Center Stage

GoodData

Today’s data-savvy companies actively seek ways to enhance their approach to analytics and evolve out of the past. While the first wave of BI involved static, rigid, IT-owned systems, and the second expanded the capabilities to a more business-facing analyst set, the third wave aims to infuse analytical activity into multiple layers of non-technical business roles. As a key strategy to enable this third wave, companies today are exploring an embedded approach that places analytical activity directly in the context of everyday user applications, and the independent software vendors (ISVs) and enterprises providing those applications have taken notice of this trend. Done efficiently, embedded analytics promotes faster deployment and pervasive usage of analytics for end-users while delivering a competitive advantage and the opportunity for new revenue streams for software vendors and enterprises.
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