All about data!

Came across this terminology while reading about APIs based on microservices architecture. So, what are two pizza teams? Yes, they are small enough to be fed by 2 large pizzas! But how are they related to microservices?! We will find out. Amazon, being a popular online store, wanted to be able to innovate in IT space quickly. But their monolith architecture was not allowing them to make software changes quickly. Monolith architectures are the old school enterprise architectures where all the components of the software are tightly coupled. These APIs are powerful and perform lot of disparate functions. Even a small change in such systems needs a lot of planning, impact analysis, availability from other teams. This definitely slows down the software release cycle. Typically in such environments there are different teams like test team, deployment team, UI team, backend development team etc who work in silos. Any small or big change goes through a long process of build, test, deploy

So, I have spent my day today listening to this global online live conference by Google. I know quite a bit about Amazon AWS platform, but didn’t really had much idea about Google cloud platform before joining this conference. And needless to say, google cloud on air, was awesome. Well, some sessions were not as good, but most of them were very informative and they encourage you to explore the google cloud platform even more. The sessions were not only talks and presentations (which were extremely informative anyways), but they were filled with demos and hands on exercizes to get us started on the platform.   ‘Cloud on air’ had 3 parallel tracks. It was a tough choice to choose between them. But then the cloud on air was being broadcasted in 3 timezones, so you can easily watch the session you have missed in the subsequent broadcast. The three tracks were Machine Learning/ AI, Big Data and Industry Solutions. And all of them were amazing.   Right from developing the services ha

Recommendation systems are everywhere. Right from amazon.com , netflix, youtube, super market’s advertising..and where not! When it all started a few years back, I used to think, wow! How does amazon knows which dress I might like next? That was really interesting. Now we all know that it’s the magic of data science. Machine learning algorithms are continuously capturing the data, learning from it and providing the recommendations. Today we are going through it in a bit more detail.   So, how data can predict what the user is gonna like or gonna buy? Based on prior knowledge. And here comes our first Association Rule Learning Algorithms, Apriori. The name tells us, that it’s going to predict something based on prior knowledge. Apriori works on 3 factors. Support, Confidence and Lift. Lets say, out of 100 people, 10 people like the movie ‘fantastic beasts and where to find them’. So, we can predict that the probability is 10%, for any other set of netflix watchers. This is g