DevOps ensures scalable, secure, and resilient infrastructure for AI/ML workloads. By implementing CI/CD pipelines, infrastructure-as-code (IaC), and cloud automation, businesses reduce downtime, improve security posture, and enable rapid innovation. Without DevOps, AI models remain trapped in research notebooks rather than production-ready systems.
During my time at SpatialEdge, I was thrown in the deep end as a contractor for the big data operations team of one of the largest telecommunications provider in South Africa. While it is hard to define what we do as a team in a single sentence, we use administrator privilege to solve all issues that do not specifically fall in the domain of other teams. If there is a production incident at 3am, a team representative will certainly be on a call. While my team is responsible for maintaining a wide array of technologies, mostly the installation, initial setup, and maintenance thereof, our key technologies are the following:
I’m well versed in iterative infrastructure automation where it immediately presents value. It happens in stages, first proof of concept and documentation, secondly building a partially automated system to improve the speed at which a human with programming knowledge to complete a task, third and finally a complete end to end system. This presents a general blueprint for different system challenges, whether it is filling reports, managing storage, or detecting vulnerabilities.