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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.

Intro

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:
*Updated on 16th December 2016 - see below With the announcement of Amazon Athena at this year's AWS re-invent conference, I couldn't help but notice its striking similarity with another rival cloud offering. I'm talking about Google's BigQuery. Athena is a managed service allowing customers to query objects stored in an S3 bucket. Unlike other AWS offerings like Redshift, you only need to pay for the queries you run. There is no need to manage or pay for infrastructure that you may not be using all the time. All you need to do is define your table schema and reference your files in S3. This works in a similar way to BigQuery's federated sources which reference files in Google Cloud Storage. Given this, I thought it would be interesting to compare the two platforms to see how they stack up against each other. I wanted to find out which one is the fastest, which one is more feature rich and which is the most reliable.
Last week I was very lucky to be able to attend the YOW! 2016 Conference in Melbourne. I had never attended a major conference aimed purely at software developers before and when I arrived early on the first day I wasn't quite sure if I had the right building. Thankfully within 30 seconds of walking in the door I spotted a man wearing a fedora and I knew I had come to the right place! The conference overall was an extremely well run affair. The speakers were all very good and many were either from high profile companies such as Facebook and Uber or were outright living legends of the industry such as Robert 'Uncle Bob' Martin. There were three talks to choose from during each time slot and they covered a wide range of topics. The hardest bit was choosing which talk sounded most interesting and I suffered from severe 'Fear of Missing Out' syndrome when making my selections. I would highly recommend attending to anyone who is looking to gain a better sense of what's going on in the software industry. Setting aside two whole days to listen to presentations, talk to other developers and generally ruminate about the craft of developing software is a great way to take a step back from the daily grind and spend some time looking at the forest instead of the trees. I picked up a number of things that I'll be able to take back and directly apply in my day-to-day development. I've summarised one of my favourite talks below: