Google Cloud Big Data and Machine Learning Blog

Innovation in data processing and machine learning technology

How RealtimeCRM built a business card reader using machine learning

Friday, June 22, 2018

Google Cloud customer RealtimeCRM explains how they built a business card scanner with node.js, Cloud Vision, and Cloud Natural language.

ML Explorer: talking and listening with Google Cloud using Cloud Speech and Text-to-Speech

Wednesday, June 20, 2018

Try out some new features of the Cloud Speech and Cloud Text-to-Speech APIs in this ML Explorer post. You'll need a development machine with audio working.

Putting a Groovy twist on Cloud Vision

Monday, June 18, 2018

Learn from Groovy's Guillaume LaForge how to build simple Cloud Vision API examples, including OCR, labeling, and landmark detection with Apache Groovy.

Introducing Cloud Dataflow’s new Streaming Engine

Thursday, June 14, 2018

Our new streaming engine for Cloud Dataflow lets you separate compute and state storage for streams, for more responsive autoscaling on fewer resources.

Securing cloud-connected devices with Cloud IoT and Microchip

Thursday, May 31, 2018

Learn how to secure your cloud-connected embedded devices with Cloud IoT and a secure element from Microchip, along with tips on secure manufacturing.

New machine learning specialization on Coursera teaches you to build production-ready models on GCP

Wednesday, May 23, 2018

We made our previously internal Machine Learning Crash Course public. Now we announce ML instruction for Google Cloud with a Coursera specialization.

Google Cloud Platform and Confluent partner to deliver a managed Apache Kafka service

Tuesday, May 22, 2018

Confluent provides GCP customers with a managed version of Apache Kafka, for simple integration with Cloud Pub/Sub, Cloud Dataflow, and Apache Beam.

Cloud ML Engine adds Cloud TPU support for training

Monday, May 21, 2018

Google Cloud now offers managed, serverless support for training TensorFlow machine learning models in Cloud ML Engine on Cloud TPUs.

Google Cloud for Life Sciences: new products and new partners

Monday, May 14, 2018

Google Cloud now serves a multitude of innovative healthcare and research partners using BigQuery, Variant Transforms, and more. Visit us at BioIT World.

Transform publicly available BigQuery data and Stackdriver logs into graph databases with Neo4j

Friday, May 11, 2018

Learn how to use Neo4j to integrate a BigQuery public dataset with Stackdriver logs into a graph database, to surface new conclusions from complex data.

BigQuery at speed: new features help you tune your query execution for performance

Monday, May 7, 2018

New performance dashboarding features allow you live insight into the performance of your BigQuery queries.

Building an image search application using Cloud Vision API

Friday, May 4, 2018

Cloud Vision API lets you classify numerous types of images with its label detection feature. Learn how to add category filters with GloVe.

Queue your questions: common queries from Google Cloud customers

Thursday, May 3, 2018

Read common questions from customers on the show floor at Gartner's Data and Analytics Summit; they cover big data, machine learning, compliance, and more.

Announcing SAP CodeJams for Google Cloud Platform: learn to integrate SAP HANA with GCP

Wednesday, May 2, 2018

Learn more about developer events that help you integrate SAP HANA, App Engine, BigQuery, and ML Engine, among others, as part of our ongoing partnership.

Cloud Composer is now in beta: build and run practical workflows with minimal effort

Tuesday, May 1, 2018

Learn more about the beta of Cloud Composer, a managed Apache Airflow service to facilitate your multi-cloud strategy.

Accessing external (federated) data sources with BigQuery’s data access layer

Friday, April 27, 2018

Understand the resource demands of BigQuery with new features that allow federated access and performance profiling.

Now live in Tokyo: using TensorFlow to predict taxi demand

Wednesday, April 25, 2018

Learn how NTT DOCOMO predicts taxi demand with TensorFlow, Cloud ML Engine, and HyperTune in Tokyo.

BigQuery lazy data loading: SQL data languages (DDL and DML), partitions, and half a trillion Wikipedia pageviews

Wednesday, April 11, 2018

Learn how to use new BigQuery features to query federated tables, and work with DDL, DML, data partitions, and a massive Wikipedia data set.

Serving real-time scikit-learn and XGBoost predictions

Thursday, April 5, 2018

Cloud ML Engine now supports scikit-learn and XGBoost in beta. Learn how to start using online prediction with these two additional frameworks.

Stretching Elastic’s capabilities with historical analysis, backups, and cross-cloud monitoring on Google Cloud Platform

Wednesday, April 4, 2018

Elastic is partnering with Google Cloud Platform to enable Elasticsearch and X-Pack functionality, as well as BigQuery and Stackdriver integration.

Using BigDL for deep learning with Apache Spark and Google Cloud Dataproc

Tuesday, April 3, 2018

Learn how to use Intel's BigDL to scale machine learning workloads with Apache Spark across multiple nodes and try out this workflow on the MNIST dataset.

Architecting live NCAA predictions: from archives to insights

Friday, March 30, 2018

Learn how the NCAA uses Google Cloud to build a predictive data analytics workflow that helps them get real-time insights from college hoops game data.

Simplifying machine learning on open hybrid clouds with Kubeflow

Thursday, March 29, 2018

Cisco and Google Cloud are now collaborating to provide a hybrid architecture for Kubeflow, permitting flexible transfer of TensorFlow jobs to the cloud.

Predicting community engagement on Reddit using TensorFlow, GDELT, and Cloud Dataflow: Part 3

Thursday, March 29, 2018

In part 3 of a 3-part series, learn how to use NLP, TensorFlow, GDELT, and Cloud Dataflow to automatically predict subreddit categorization of news posts.

Testing future Apache Spark releases and changes on Google Kubernetes Engine and Cloud Dataproc

Wednesday, March 28, 2018

Learn how to test out upcoming changes and versions of Apache Spark in Google Kubernetes Engine, preferably on test rather than production data.

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