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On my journey with Data Science, I heard Reinforcement Learning is used in likes of Game Development and Autonomous Driving. As per Wiki definition:

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.[1]

So Non-Player Characters (NPC’s) within a game environment becomes obvious candidates for such intelligent agents. And then I watched this video by Paris Buttfield-Addison at North Bay Python (2019) that got me hooked.

In this post I will describe my experience of developing a simple Unity

Too many apps in the world
Too many apps in the world
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I got motivated by success stories of brilliant AI enabled mobile apps sometimes developed by kids under the age of 15 appearing in news and adverts every now and then. Told me: “I should build one — it isn’t as easy as it sounds”. Picked up Automatic Number Plate Recognition as a use case. Over 2 weeks of part-time research and development (deployment not included) for the pet project I didn’t come across end-to-end AI app examples (not saying I was thorough with my search). …

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Certain use cases require strict ordering of events (messages/records with data payload and/or state) to be maintained across producers and consumers in a data pipeline. For example in financial institutions, order of transactions need to be preserved to compute account balances correctly. This article focuses on aspects that can be considered by producers and consumers to maintain such ordering.


As per Kafka guarantees:

Messages sent by a producer to a particular topic partition will be appended in the order they are sent.

To ensure strict ordering across all events, the topic should be single partitioned. Well, it’s not too scalable…

Siddhartha Bhattacharya

Technical Architect, Data Platforms

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