In this series of posts, we'll cover how Gretel's synthetic data platform helps you overcome challenges across the data-centric machine learning life cycle to help you successfully build, deploy, maintain, and realize value from your AI projects. The life cycle outlined below is a common framework or workflow process for building machine learning and AI solutions. It's focused on streamlining the stages necessary to develop machine learning models, deploy them to production, and maintain and monitor them. These steps are a collaborative process, often involving data scientists and DevOps engineers. The process below was inspired by the value chains created by The Sequence, Databricks, Google Cloud, and Microsoft.

A great way to keep your wits about you when working with machine learning (ML) and artificial intelligence (AI) is to think like a teacher. After all, the point of ML/AI is that you're getting your (machine) student to learn a task by giving examples instead of explicit instructions. As any teacher will remind you: if you want to teach with examples, the examples must be good. The more complicated the task, the more examples you'll need. If you want to be able to trust that your student has learned the task, the test must be good.

For example, those processes where RPA could not be implemented because the underlying data was unstructured and human judgment was involved can now be automated by introducing AI, which would convert the unstructured data to structured data and make decisions as a human would. In addition, AI robots would self-learn over time and thus would further free up resources that are needed to work on the'exceptions', which RPA could not handle. Integrating RPA and AI would also allow the automation process to become significantly faster and end-to-end. Similarly, the lone implementation of AI may not make business sense because of the implementation costs involved, but when combined with RPA, would be viable to implement, as combined ROI of the two technologies would be positive.