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AI Enthusiasts with writings covering AI, Data Science, and Freelancing

If You’ve Never Failed, You’ve Never Lived

It’s quite normal to cast failure off in a bad light, it hurts to fail. But in reality, failure is not only inevitable for anyone that dares to try something new, it’s necessary. Embracing failure is the catalyst to exponential growth because within them lie valuable life lessons. Here’s the great thing about learning from failure… They don’t have to be your own.

Although we’ve known about Artificial Intelligence (AI) for many years, its application in industry is still very much in the nascent stages. Failures are to be expected. …

How to safely deploy machine learning Models

Reproducibility is the accountability required from businesses to further understand and trust the adoption of Machine Learning into our day-to-day lives.

As Machine Learning becomes more productionized, many businesses and researchers may feel compelled to rush in on the action, to the detriment of full comprehension of the intricacies involved in the implementation of certain methods, or what is sacrificed by rushing through processes without the right procedures in order, all in hopes of faster results.

With access to the information at our fingertips and Machine Learning failures making major headlines, like in the case of Microsoft’s Twitter Chatbot, the…

The Forgotten Phase

To fully grasp the power of Machine Learning, we ought to learn about the model deployment phase. This phase of the Machine Learning workflow is often left out of the popular Machine Learning courses, leaving many confused about what to do next once they've developed their champion model.

Just to clarify, Machine Learning is leveraged to solve problems by the use of data and improving automatically through experience. For a model to be able to solve problems in the real world it must be able to be accessed and for this to occur it must be effectively deployed into production.

A Look At The 3 Main Pricing Models

The veteran freelancers know what it’s like when you first venture into freelancing. There are so many things to be scared about; what if I can’t complete a job? what if I don’t get any jobs? What if I get ill and can’t work? How am I going to do my taxes?

Of course, I had these thoughts too. Strangely, they didn’t plague me as much as others make out. Before I decided to start freelancing I already had several clients lined up so there was nothing to worry about on that front.

What kept me up at night was…

A Different Approach To Job Seeking


I was casually browsing through my LinkedIn feed when I came across a very interesting post by Carlos Mercado. The first line read “If I had 90 days to find a job, I would spend 60 of those days reading, building, and writing”. While I have no interest in looking for full-time work, this post caught piqued my interest…

What would I do If I had to find a job in the next 90 days?”.

Carlos went on to detail how he would break up his days into 8-hour shifts for the next 60 days and I must say I…

Taking Machine Learning Into Production

I’m a big advocate for learning by doing, and it just so turns out that it’s probably the best way to learn machine learning. If you’re a machine learning engineer (and possibly a Data Scientists), you may never quite feel fulfilled when a project ends at the model evaluation phase of the Machine Learning Workflow, as your typical Kaggle competition would — and no, I have nothing against Kaggle, I think it’s a great platform to improve your modeling skills.

The next step is to put the model into production, which is generally a topic that is left out of…

Productionizing Machine Learning Models

In The Machine Learning Workflow, I covered how to take Machine Learning from inception to production in the real-world. While it’s possible that machine learning can be done locally on your own hardware, when it becomes time to scale, doing Machine Learning in the cloud is by far a better option for reasons we will cover later in this article.

In the meantime, let’s build our understanding of what the cloud is before we get into why it’s prefer when it’s time to scale.

What is Cloud Computing?

The crazy thing about cloud computing is that the majority of us use it already with…

Building A Machine Learning Engineer

A large influence in what has permitted me to completely change my career trajectory has been reading. Building an effective reading habit is known to be a key KPI in measuring the success of individuals — it’s said that CEOs read at least 50 books per year.

For the Machine Learning Engineers that endeavor to take their careers to the next level, or those who are just starting out and need more exposure to the engineering aspect of machine learning, I’ve curated 5 extremely influential books that I believe will do exactly serve those purposes.

Disclaimer: Please note that by…

From Inception to Production


Approaching a Machine Learning project for the first time on your own can be extremely overwhelming. When you’ve taken and passed a multitude of online assessments, it can be quite confusing as to why you still feel as though there is still something missing the minute you begin working on a problem — this tends to lead to a vicious cycle of taking course after course without making much gaining much practical experience.

Courses are great and I love taking courses when I want to upskill in an area, but over time, I’ve realized that courses can only do much…

Building a Machine Learning Engineer

Do you enjoy creating software but are extremely intrigued by Data Science? If so, you may want to consider the role of a Machine Learning Engineer. Machine Learning engineers sit at the intersection of Software Engineering and Data Science — meaning you’ll need both skills if you really want to excel.

The focus of Data Scientists is to transform disparate data into actionable insights. On the other hand, the Machine Learning Engineer focuses on developing working software that makes use of the data as well as automating predictive models.

Here’s a summary of the skills required:

Software Engineering

Computer Science fundamentals are…

Kurtis Pykes

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