Experimenting with Data Science techniques to answer business questions shows organizations the value they can expect from their investment in Data Science, and the best way to go about it.

However, to realize true day-to-day value they need to consider more factors such as: the dependability of data, the cadence of the data, the bespoke application of Data Science techniques, and the automation required to create a dependable solution.

In this short video, we will look at how you could define an experiment and what you would do to scale your experiment up in a productionized manner to ensure the solution provides business value over time. We will touch on MLOps, features and their management, the role of data engineering, and other aspects an organization should consider in their building of Data Science Solutions.

If you liked this video, make sure to check out the rest of ‘The Stages of your Analytics Journey’ video series. In this series we talk through each stage of the journey that an organization can undergo to effectively apply machine learning (ML) to their data to find insights and inform decisions. From the beginning steps of creating an ML model, all the way through to having a successful productionized solution that the business understands and uses repeatedly, and every stage in between. Parts 1, 3, and 4 are linked below.