Data Science & ML

ML engineers, AI researchers, predictive modeling

Although data science and machine learning are sometimes used interchangeably, they are different disciplines with distinct focuses. Data science is a wide-ranging and multidisciplinary field that uses data to solve organisational problems. It necessitates understanding business context, cleaning and structuring data, visualising data and carrying out exploratory analyses.

Machine learning is a subset of artificial intelligence (AI) and is a set of analytical tools that data scientists use to train their automated predictive models. Machine learning models train on previously-collected data to identify patterns which will allow them to more accurately predict future outcomes.

Predictive modelling uses historical data, machine learning and algorithms to detect patterns and trends within data, blending statistical techniques and a computational pipeline to help organisations make data-driven decisions. It’s a structured pipeline rather than one algorithm, where teams define their objective, gather and prepare data, determine which features will have the strongest influence on the target outcome, train the model on known data and then test and evaluate the model’s performance on unseen data to measure its accuracy.

Machine learning engineers are specialised software engineers, tasked with building, deploying and maintaining the AI systems that learn from data. They are the intermediary step between data science and production software engineering. They develop models that design, train and test machine learning and deep learning algorithms, and use frameworks such as TensorFlow and PyTorch. They deploy these models, integrating them into production environments and scalable software architectures. Once they’ve been deployed, engineers will monitor them to ensure accuracy and that the models are continuously retrained.

AI researchers come up with new ideas that may open up new possibilities in the use and application of artificial intelligence. Once they’ve had an idea, they will conduct research to establish what has previously been done and if there are gaps where this idea could fill in, before developing a research plan.