Featured » Data Science Resources » Using Pivotal Cloud Foundry with Google’s BigQuery and Cloud Vision API

× Share this Video

Facebook
Twitter
Email
LinkedIn

Using Pivotal Cloud Foundry with Google’s BigQuery and Cloud Vision API

November 30, 2016

Share this Video
Facebook
Twitter
Email
LinkedIn

Enterprise development teams are building applications that increasingly take advantage of high-performing cloud databases, storage, and even machine learning. In this webinar, Pivotal and Google will review how enterprises can combine proven cloud-native patterns with groundbreaking data and analytics technologies to deliver apps that provide a competitive advantage. Further, we will conduct an in-depth review of a sample Spring Boot application that combines PCF and Google’s most popular analytics services, BigQuery and Cloud Vision API. Speakers: Tino Tereshko, Big Data Lead, Google Joshua McKenty, Senior Director, Platform Engineering, Pivotal

Wealth Creation and Academic Health Science Networks

Delivery in a Land with No Postcodes

Have you ever taken a taxi in the Arab world? Specifying a postcode won’t help you much. Instead you have t...

Using Pivotal Cloud Foundry with Google’s BigQuery and Cloud Vision API

Previous

Next

Using Pivotal Cloud Foundry with Google’s BigQuery and Cloud Vision API

Previous

Next

Related content in this Stream

This 7-part blog series provides a roadmap for architecting a data science platform using VMware Tanzu. We'll delve into the building blocks of a successful platform that drives data-driven insights.

This 7-part blog series provides a roadmap for architecting a data science platform using VMware Tanzu. We'll delve into the building blocks of a successful platform that drives data-driven insights.

This 7-part blog series provides a roadmap for architecting a data science platform using VMware Tanzu. We'll delve into the building blocks of a successful platform that drives data-driven insights.

Wondering what the White House’s executive order on artificial intelligence means for your business? This blog summarizes what you need to know and provides ideas for how to get started.

Synchronizing artificial intelligence and data science to multiple facets of the application life cycle aids enterprises with generating more business value from their applications.

At VMware Explore in Barcelona, we’re announcing new artificial intelligence and machine learning offerings in the VMware Tanzu portfolio that can help organizations drive business innovation.

A new vector database introduced in VMware Tanzu GemFire enables organizations to unlock the full potential of generative AI.

There are differences between working on a traditional software product and one that incorporates data science. Successfully folding data science into a product team is a little like hunting a bear.

In this playbook you’ll find our advice for effective ways to bring this capability—and the humans who drive it—closer into your fold.

Securing Cloud Applications demystifies complex security protocols, algorithms, and patterns, and demonstrates how to put them into practice in everyday development.

DKube on VMware Tanzu enables you to save time, resources, and cost with IT and data science teams collaborating with best-in-class model operations and infrastructure management.

With Domino Data Lab and VMware Tanzu, code-first data science teams can accelerate research, increase collaboration, and deploy models across an optimized multi-cloud infrastructure.

Hi, Spring fans! In this installment, Josh Long (@starbuxman) talks to webassembly, IoT, data science, and Java guru Brian Sletten (@bsletten).

Greenplum is open-source software for massively parallel database used for reporting, analytics, machine learning, artificial intelligence, and high concurrency SQL. Greenplum database is...

author：Hans Zeller Optimizing joins is the core part of any query optimizer. It consists of picking a good join order, the right join algorithms (hash join, nested loop join, etc.) and various...

In a previous post, we discussed the advantages of running JupyterHub on Kubernetes. We also showed you how to install a local Kubernetes cluster using kind on your Mac, as well as how to install...

Provisioning environments for data scientists and analysts to run simulations, test new models, or experiment with new datasets can be time-consuming and error-prone. Python is a popular choice...

Author: Jared Ruckle Every enterprise is refining their AI strategy. So it’s only fitting that the final installment of Greenplum Summit 2020 focused on how artificial intelligence and neural...