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My learning experience with Google’s Machine Learning Crash Course

Google’s Machine Learning Crash Course (MLCC)

I came to know about Google’s Machine Learning Crash Course (MLCC) from Sundar Pichai’s tweet. I then enquired about it with some close acquaintances working in Google. I was soon pretty convinced of pursuing this course, after their good words about it and my own research on the course content. This post is going to be an account of my learnings from MLCC. I will structure the learnings in such a way that it will look more like a review. I will also include what I really liked about the course and things which I think they can possibly improve, if the creators are planning to update the course content. So, let’s get started!

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Engineering challenges of streaming a million concurrent JSON data streams from product to CRM

At Truebil, I was fortunate enough to be given an opportunity to solve a unique engineering problem. We had already outsourced CRM development to a third party but I had to integrate the data flow to CRM from our product and back. I mentioned earlier that I was given a unique engineering problem because of the challenges it posed. The challenge didn’t lie in the CRM integration alone, but the fact that Truebil has umpteen number of in-house products which spawn data close to about 1 Million data streams per hour. Besides building a bastion of such magnitude, I knew that it would be equally challenging to work with three different verticals and stakeholders. This post is going to be an account of my experiences dealing with two things. First, how Truebil catered to the transfer of a million data stream to and from CRM without hampering its operations and customer support. The second experience deals in the art of working with different verticals. Not that I have mastered the art or something, but I will share my own experiences and learnings in this post.

  

 

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