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Yaay!!! Welcome to the new year 2019, this is going to be my first post in the year, I am glad about it as I get to start the year on a very high vibe.
I recently had to work on a project to build a face-recognition engine that will be used in production. Here I am going to describe on an high level things that were done.
Here are some of the vital points I got from the book Think Like a Data Scientist by Brian Godsey.
Having worked with Machine Learning model for quite sometimes, the basic challenge has been deployment of the model in production. With this in mind google created Tensorflow Serving which is supposed to be an ideal environment for running models in production.
Apache Kafka messaging system with Apache Spark are two platforms that came with the Big Data Wave and they have been very useful in managing Big Data expecially when it comes to Streaming Data or fast data. I recently had to make use of this two framework for a project and I am going to explain briefly how I set up both projects locally and deploying to production. The project makes use of two machine learning model, One is a Neural Network and the other is Clustering. I won’t go into the details of the project as at yet, I will leave that for another post. But here is the summary of the project, my neural netowrk is supposed to generate signatures on some images and then my Clustering should then group them together into segments as they are been uploaded, such that at a particular time, I should be able to upload an image and predict the clusters which it will belong. I hope this summary gives an idea where these frameworks will be important.
Linear Model is a foundational model when it comes to Machine Learning, this simple article is to explore building a simple Linear model with Tensorflow. The basic idea is to lay a foundation of a model that is very important in understanding deep neural network. Deep Neural Network (DNN) is intuitively getting a good representation of your input data that a model can use to predict rightly the information contained in the data as would a human. Okay, that sounds a little complex, DNN takes your data, which may be image, text and even stock market data, and passes it through layers of neurons - here neurons means just function, such as the Linear function we are trying to model here - and uses this new representations to predict perfectly the information contained in the input data.
Tensorflow is one of the many state of the art Deep Learning software Library that makes learning a DNN fast and scalable. It’s well optimized and can abstract a lot of codes that would have gone into training a neural network by coding all the mathematics, note that Tensorflow itself is a library for numerical computation. It is built to be scalabel and distributed and can work perfectly in production.
Given a modified machine learning model, how do you know if your newly trained model will make any difference?
Collection of useful deep learning resources
First Published in 2018
Reference and resources for working with Machine Learning in Scala/Java
First Published in 2018
First Published in 2019
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.