Adekunle Babatunde

Adekunle Babatunde

Data Scientist and Machine Learning Engineer

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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Posts

Rethinking Data Engineering At Scale

4 minute read

Published:

I started 2021 with a focus on establishing myself in data engineering and building pipelines both for data science and machine learning operations. My last article was entirely me evaluating what goes on in the world of data engineering, the tools I have seen organiztion use and whether there is a case for build your own vs use an open source tool.

Scalable Data Engineering: A Case For Build Your Own Platform

4 minute read

Published:

Delving deep into the realm of Data engineering, most especially using Scala as the programming language of choice, there seems to be a very basic thing for new entrants into the field, this applies both to the company trying to set up a data engineering platform and the engineer/consultant trying to propose a way to do this effectively. The question of how do I build my data platform right.

Machine Learning Model Deployment With Cpp Part 2

9 minute read

Published:

Picking up my initial article where I build a PCA model using CPP, In this article, I will be loading the saved model whose values are stored in a yaml file. This script will be developed to form an inference engine bundled as a .so file for deployment on a linux based environment. For other platforms, the cpp codes can be compiled to produce either a dll or dylib for windows and mac respectively. Since we are more concerned about deploying the model on an android application, the focus will be building the .so file inference engine from our saved model.

Machine Learning Model Deployment With Cpp Part 1

7 minute read

Published:

Recently I have been fascinated with how interesting it is to build mathematically inclined application and deploy at scale and without any restriction of model size, platform or need for api calls. I know that python has enough library for working with prototypes of machine learning project, however not many are talking about scaling this project especially when you don’t want to do that over a web api. I believe true intelligence shouldn’t rely only on calls to an api for a model to be available in scale, this fascination led me to research into what it will take to use C++ for machine learning and general intelligence. My convitiction is that both matlab and python’s mathematical strenght are based on an underlying c/c++ code hence fundamentally scaling technology to work with mathematical computations involved in machine learning with blazing fast scenerio in mind will likely require that you are able to dig into low level programming and most especially with c/c++, I choose c++. Also, I wondered why fundamentally most computer science schools ensures that there is a c/c++ curricullum in there study, this emphasizes the reasoning of using c/c++ for scalable technology.

Linear Regression With Tensorflow Updated

7 minute read

Published:

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.

My First Experience With Kafka And Apache Spark Stream

5 minute read

Published:

Introduction

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 With Tensorflow

5 minute read

Published:

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.

Why Tensorflow?

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.

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