Enabling connected transformation with Apache Kafka and TensorFlow on Google Cloud Platform

KAI WAEHNE | February 22, 2019

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Many organizations depend on real-time data streams from a fleet of remote devices, and would benefit tremendously from machine learning-derived, automated insights based on that real-time data. Founded by the team that built Apache Kafka, Confluent offers a a streaming platform to help companies easily access data as real-time streams. Today, Confluent’s Kai Waehner describes an example describing a fleet of connected vehicles, represented by Internet of Things (IoT) devices, to explain how you can leverage the open source ecosystems of Apache Kafka and TensorFlow on Google Cloud Platform and in concert with different Google machine learning (ML) services.Imagine a global automotive company with a strategic initiative for digital transformation to improve customer experience, increase revenue, and reduce risk. Here is the initial project plan. The main goal of this transformation plan is to improve existing business processes, rather than to create new services. Therefore, cutting-edge ML use cases like sentiment analysis using Recurrent Neural Networks (RNN) or object detection (e.g. for self-driving cars) using Convolutional Neural Networks (CNN) are out of scope and covered by other teams with longer-term mandates.

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Since its inception in the year 1999, LDS Infotech Pvt. Ltd. has been providing world class IT products, services and consulting to diversified range of business. Our mission is to help implement technology for commercial use thereby helping our clients manage change through high-quality, cost effective, simple and practical solutions.

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