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Improving the efficiency of your helpdesk with serverless machine learning Google Cloud Big Data and Machine Learning Blog Google Cloud Platform

@machinelearnbot

Great customer service builds trust, inspires brand loyalty, and earns repeat business. So it's no surprise that, according to Deloitte, close to 90 percent of organizations name improving the quality of their customer service as a strategic focus. Customer service helpdesks know this all too well. They often deal with an ongoing flow of tickets that sometimes have little information or context, which can slow down agents and impact service quality. What if you could use historical data to predict key KPI or fields of a support ticket to handle it in the most efficient way?


Cloud AutoML - Custom Machine Learning Models Google Cloud Platform

#artificialintelligence

Cloud AutoML is a suite of Machine Learning products that enables developers with limited machine learning expertise to train high quality models by leveraging Google's state of the art transfer learning, and Neural Architecture Search technology.


wwoo/tf_face

#artificialintelligence

Firstly, follow the setup guide to install the Google Cloud Machine Learning SDK. This will also ask you to install TensorFlow. Make sure to specify the correct number of training classes (--num_classes) and number of samples in your validation set (--valid_batch_size). This will differ depending on the number of files you've downloaded and how the data has been divided. Check the training source for other flags you can specify. Your trained model will be exported to /tmp/model/00000001 by default.


Using Apache Spark with TensorFlow on Google Cloud Platform Google Cloud Big Data and Machine Learning Blog Google Cloud Platform

@machinelearnbot

Apache Spark and TensorFlow are both open-source projects that have made significant impact in the world of enterprise software in recent years. TensorFlow provides a foundational framework for running distributed numerical computations, such as deep learning algorithms, while Spark is a general Hadoop-like, large-scale data processing framework that's also a popular choice for more traditional machine learning algorithms using MLlib. Google Cloud Platform offers managed services for both Apache Spark, called Cloud Dataproc, and TensorFlow, called Cloud ML Engine. Both of these services deliver the power of their respective open-source frameworks in a managed environment, letting you focus on the data science while we worry about the operations. Intuitively, there is some overlap -- Spark provides a framework for big data computations, and the type of datasets that power TensorFlow algorithms tends to be large.


Google Cloud Platform breaks through with big enterprises, signs up Disney and others

ZDNet

Diane Greene made her pitch to CIOs on Wednesday and argued that Google's Cloud Platform is ready for primetime. Coming off a few key wins recently, Google Cloud Platform has been growing in its offerings and competing with AWS and Microsoft Azure in the price cutting wars. However, the company has struggled to make its case with major enterprise clients. On Wednesday, at the 2016 Google Cloud Platform Next conference in San Francisco, Google announced that Disney Consumer Products Interactive Media and Coca Cola would be joining the fold as the newest Google Cloud Platform customers. The news comes only a few months after Google netted big deals with both Spotify and Apple, with the two implementing Google Cloud Platform within their organizations.