Build a career in machine learning

What started as a love affair with computers in a cybercafe in Lagos as a child evolved into a career in machine learning, data science, and MLOps. Gift Ojeabulu originally wanted to be a doctor, but along the way he had stints as a rapper and dancer before finding his way back into the tech industry.

Currently, he is a machine learning engineer working with global teams and co-founder of Data Community Africa, an initiative to advance data and AI education across the continent.

In this edition, outside hoursOjeabulu explains his journey to using data models to predict Champions League scores and becoming a machine learning engineer.

Early encounters with technology

My first memories of technology date back to elementary school. I grew up in Ketu, Lagos and back then there were cyber cafes everywhere. For many people, these cafes are associated with some kind of internet culture, but for me they were a window into something fascinating.

My older brothers introduced me to computers earlier than most of my colleagues. Every time I got paid for school, I would sometimes sneak into a cafe and browse for a few minutes. That was my first real interaction with a computer, mainly clicking and learning mouse control.

At school, I stood out in ICT classes. At the time, I didn’t fully understand what that meant. I found myself just having fun.

But to be honest, my interest in gaming started long before that. I had a Sega at home, and I always played with it. I also went to the game store with my brother and played Winning 11 on PS1 and PS2. Those were good times. By SS1 I had my own laptop, but I mainly used it for FIFA and Mortal Kombat.

Around the same time, I was also DJing and performing at friends’ parties and birthdays. I didn’t think of it as technology at the time, but I was always tinkering with equipment and figuring things out. I also loved science fiction movies. “The Matrix” and “Blade” were works that really stuck with me. Looking back, all of that quietly shaped the way I viewed technology. I didn’t realize it at the time.

From medicine to machine learning

My academic journey did not begin with technology in mind. I originally wanted to study medicine and surgery, like many Nigerian students. Since I didn’t meet the standards, I switched to medical biochemistry. Ultimately, academic requirements led me to transfer to Computer Science at Ambrose Alli University.

It wasn’t planned. In fact, when I entered the world of computer science in my sophomore year, I had to ask myself, “Where do I start?” The answer came from curiosity. I have always been a multifaceted person. I rapped, danced, played the drums, and acted.

At the time, I had friends who were already building using web technologies like HTML and Bootstrap. I followed him, watched him work, and watched how lines of code were translated into real products.

I immediately started teaching myself using Udemy, Udacity, and whatever else I could find. I looked into web development, then moved to Android development and delved into Java. I was fascinated by the idea of ​​building something from scratch.

In 2017-2018, I built my first real project, an Android app. It wasn’t deployed and I didn’t even know about GitHub at the time, but it worked. My friends and I were able to use it.

Then my laptop crashed. Everything I had built was gone. At that point, I didn’t have the money to replace it, so I took a break from the tech industry and focused on school. Looking back, that moment could have ended my journey, but thankfully it didn’t.

A friend introduced me to AI Bootcamp in 2018. That was my introduction to data science and machine learning. I started studying online, watching lectures, learning concepts like eigenvectors, and digging into the basics.

By 2019, I entered contests and ranked highly. My team also won the Best AI Poster Award at a national event held in Lagos. That experience made it clear that this was the path I wanted to pursue. From there I doubled down. In 2020, I took a more structured course that included a program that helped me understand not only the technical side of AI, but also the business side.

By 2021, I landed my first major role as a data scientist at a basketball analytics company, where I applied machine learning to sports data. That role opened doors.

I then worked as an MLOps developer advocate at a global AI company, where I spent about two years. At some point, another company approached me with an offer and asked me to specify a price. I did so and they accepted immediately. It was one of those moments where I realized how far I had come.

I currently work in multiple roles, including at a Canadian-based AI automation company focused on document intelligence and another organization working on voice systems for African languages.

live through technology

Technology is not just my career, it’s also my way of thinking.

At one point, I built a model to predict the outcome of a soccer match, not for betting purposes, but out of curiosity. Input historical data into the system, analyze patterns and results, and calculate score probabilities. To my surprise, my predictions were often accurate. That experience ultimately influenced my sports data science work.

Additionally, technology shapes how I make decisions, the type of content I consume, and even how I interact with people. I actively encourage others to explore technology not only as a career but also as a tool.

One of the most important parts of my journey has been the community. I co-founded Data Community Africa in 2022. Its membership has grown to more than 15,000 people across 40 African countries, with more than 5,000 DataCamp licenses, and annual physical conferences and hackathons that attract 1,500 to 3,000 participants.

We also launched MLOps Circles to increase African representation in the global MLOps ecosystem. Because at some point I realized that Africans were largely missing from that conversation.

For me, community is about access. Certain setbacks could have been avoided if I had received more guidance earlier in my career. So now I’m trying to build that support system for others.

One of the tools I can’t live without right now is Claude. I’ve been an early supporter of it, even before it became widely available. It’s in line with my way of thinking and helps me work more efficiently. We also use tools like NotebookLM, but it hasn’t been widely adopted here yet.

To stay up to date with technology trends, I rely heavily on newsletters such as Techpoint Digest, Twitter, and LinkedIn. I follow people who are deeply embedded in the AI ​​space and are always sharing their insights. We may also use AI tools to examine trends and improve understanding.

If there’s one thing I wish technology could do better, it’s the representation of African languages ​​in particular. We need a system that understands local context. Imagine an AI tool that can speak fluently in Yoruba, Hausa, or Swahili and truly understand the cultural nuances, not just translate. Then millions of people will have access.

Additionally, there is the issue of mobility. As a Nigerian, traveling for global opportunities can be difficult. I was unable to attend a fully funded international event due to visa issues. This is frustrating, especially in a global field like technology.

If I were to build a product today, my focus would be on two things: advancing African language AI and building systems that facilitate global mobility through better data validation and trust frameworks.

I believe that the next decade will be defined by Africa’s participation. Our population is one of the youngest in the world, and it has energy, curiosity and adaptability. As more African youth gain access to resources and global exposure, they will not only participate, but also lead.

The challenge is infrastructure: power, internet, and economic stability. It’s hard to innovate when you’re focused on survival. But once these barriers are improved, I am confident that Africans will play a central role in shaping the world’s AI and technology future.