In this post we will discuss and explore methods of navigation flow and view construction in SwiftUI. In particular, we will explore a method for abstracting both of these activities out of the view layer. ...

I recently had the opportunity to create pipelines for deploying code to the Salesforce platform using Jenkins. This post describes the challenges I encountered implementing pipelines using a number of different techniques....

Are you working on a project that handles a lot of data? Is it growing? Do you have problems transferring data? If so, you need to read this story. There was this project that I worked on, which is called “Chapar” which means mailman in...

Introduction: The objective of this article is to achieve SSO with SAML authentication in AEM  involving Single identity provider(IDP). Here we are using Shibboleth as IDP.  This article includes setting up Shibboleth IDP , integrating with ApacheDs(Directory Server) followed by integration with AEM. Little bit background on SAML...

Semi Permanent May 24-26, 2018 

Held in Carriageworks in Sydney this design conference has been going since 2003. It covers new design ideas, presentations of great media and advertising agency work and artists recalling their own journeys developing their work and careers. The presenters include directors, photographers, typographers and illustrators. It includes big names in the design industry - past conferences have hosted Pixar, Banksy, Weta digital, Oliver Stone and VICE media.
In parts 1 and 2 of this blog series, we've seen how to implement an item-similarity model in TensorFlow, and the intuition behind various recommender models. It's now time to have a high-level view of a recommendation project in the Google Cloud Platform. This will encompass all of our plumbing for the web service, so that it can be up and available on the web. I will outline two possible architectures - one where we deploy and manage TensorFlow ourselves using the Google Kubernetes Engine (GKE) , and the other using the fully-managed Cloud Machine Learning Engine (MLE).  You'll also find how to communicate with the ML engine modules, and how to configure your computational clusters.


Recommendation systems are found under the hood of many popular services and websites. The e-commerce and retail industry use them to increase their sales, the music services provide interesting songs to their listeners, and the news sites rank the daily articles based on their readers interests. If you really think about it, recommendation systems can be used in pretty much every area of daily life. For example, why not automatically recommend better choices to house investors, guide your friends in your hometown without you being around, or suggest which company to apply to if you are looking for a job.

All pretty cool stuff, right!

But, recommendation systems need to be a lot smarter than a plain old vanilla software. In fact, the engine is made up of multiple machine learning modules that aim to rank the items of the interests for the users based on the users preferences and items properties.

In this blog series, you will gain some insight on how recommendation systems work, how you can harness Google Cloud Platform for scalable systems, and the architecture we used when implementing our music recommendation engine on the cloud. This first post will be a light introduction to the overall system, and my follow up articles will subsequently deep dive into each of the machine learning modules, and the tech that powers them.

The TEL group was established in 2011 with the aim of publicising the great technical work that Shine does, and to raise the company’s profile as a technical thought-leader through blogs, local meet up talks, and conference presentations. Each month, the TEL group gather up all the awesome things that Shine folk have been getting up to in and around the community.  Here's the latest roundup: