A typical data science project lifecycle goes through many phases. The first phase is to understand the business and the data, followed by data preparation, model training, model evaluation, and production deployment. These phases are not simple. They’re tedious, repetitive, and limited to a few skilled individuals like researchers, data scientists, and ML engineers.
In practice, data scientists spend a significant portion of their time doing repetitive tasks like cleaning, sourcing and preparing data for training a model. Some of these repetitive tasks can be automated.
The demand for intelligent systems has continued to grow, yet most organizations lack the…
I was scrolling carelessly on Reddit a few days ago when I saw a thread on buzzwords that made me pause for a few seconds. In truth, buzzwords are powerful. They tend to express ideas and concepts in one word that are usually more popular than the core ideas they represent. For me, I like to see buzzwords as a way of packaging ideas with fancy words.
But this post is not about buzzwords but rather a word that many people misunderstand: DevOps.
I get emails from recruiters and companies looking for DevOps engineers all the time. This makes me…
Forecasting is a process of making predictions of the future based on past and present data and, most commonly, by analyzing trends.
History is filled with people trying to predict the future by looking at trends and patterns. We don’t have the power to change the past, but we can control future outcomes if we know the future.
For businesses, the ability to predict the future and make informed decisions is critical to their survival.
The traditional method of generating forecasts from time series data often struggles to generate accurate predictions, especially when dealing with extensive data with…
2020 turned out to be radically different from what everyone had expected. COVID-19 has impacted our lives in many ways. As the pandemic spread, one question that baffled us was: what can we do with technology to combat the virus?.
A few days later, we got the answer.
In a phone conversation in March 2020, Olalekan Elesin informed me of an idea he thought about while doing his regular grocery shopping at DM Drogrie. The idea is centered on a bracelet like your typical watch that dispenses Sanitizer. Before this time, Sanitizer comes packaged in bottles and cans of different…
The year 2020 started like any other year until an outbreak that nobody on our planet has seen hit. Although COVID-19 has been around since 2019, It didn’t spread beyond China until 2020. The world changed rapidly to a different place — the world’s economy driven to a halt. The healthcare system stretched to the brink, and workers left alone to face the scourge of COVID-19 as stories of layoffs flooded LinkedIn pages every day. As of today, 686k people have died from COVID-19, with 17M confirmed cases in the world.
This is my first post in a series I titled “creating your first machine learning model.” I assume you already know the basics of machine learning and want to leap towards creating your first machine learning model. My goal is to break the steps down for you as possible.
If you find any uncleared terms or something you don’t understand, it means I did not live up to my promise. Be aware that you’re free to comment and request for clarification.
In this post, you’ll learn:
Twitter users tweet 500 million tweets per day. The volume of information going through Twitter per day makes it one of the best platforms to get information on any subject of interest. In this post, I’ll walk you through how I built a twitter bot with a brain — powered by machine learning.
We’re currently running a set of customer interviews and market research to figure out the challenges writers have in knowing readers’ feedback about posts and newsletters.
Today I’ve decided to take it further by sharing our idea with you in this post and hopefully hear your thoughts in the comment section.
Clear feedback is the cornerstone of improvement ~ Sir David Brailsford’
A few weeks ago, I internalized Sir David Brailsford’s quote and I decided to hear the silent thoughts (feedback) of my monthly newsletter readers.
I wanted to know how my readers perceive each newsletter. I’m not a charity…
In this post, we’ll look at common techniques used in detecting edges for image segmentation.
Object detection in computers is similar to how humans recognise objects. As humans, we can tell the image of a dog because of features that uniquely characterises a dog. The tail, shape, nose, tongue, etc, all combined differentiate a picture of a dog from that of a cow.
Likewise, computer is able to identify an object by detecting features relevant to estimating the structure and properties of the object. One of such features is edges.
Mathematically, an edge is a line between two corners or…
In this tutorial, you’ll learn how to deploy a Pytorch model on AWS cloud infrastructure.
In my previous post, we saw how to train an image classifier that is capable of recognizing images of objects in 10 categories. Today, we’ll deploy the model from the training job on SageMaker hosting services (Pytorch model server) for real-time inferencing. The full source code for this tutorial can be found on this Github repository.
SageMaker is a fully managed service from AWS that allows you to build, train, and deploy ML models into a production-ready environment at scale. …