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Throughout different sectors and industries, business needs vary wildly, but they have some commonality. Most businesses collect and analyze data, which requires robust digital infrastructure. Some of the most common operations involving data analysis include: Investors can also use data analysis to predict the share price of businesses. It is common practice for companies to invest in their competitors as a hedge. Some useful metrics to study businesses include: Financial metrics Sentiment analysis Economic indicators Few buy new socks during a recession, many buy canned foods and processed meat Stock market trends

Data Engineers collect and format data. Data wrangling is one of the most tedious and time consuming tasks in the ML stack and eliminating it at this layer solves a lot of problems down stream. Data versioning and database partitioning ensures that old data can be discarded when it becomes irrelevant or non-compliant with privacy laws.

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: ...

ML Engineering turns raw data into actionable insights. Cutting edge models studied in theoretical computer science are often under utilized in the industry. This is in part due to rightful skepticism regarding the model’s explainability and ability to generalize to new data outside of their training sets. In my experience, decision trees and regression are by far the most widely used models where safety is critical. Sure, you can use LLMs to chat to your customers, but you should not use it to detect fraud just yet. ...