Get Your Data Ready for AI/ML

Understanding the readiness of your source data before you launch an expensive AI/ML project lets you take corrective data engineering measures that will streamline the project and give you the best probability of a successful outcome.  Artificial Intelligence (AI) and Machine Learning (ML) projects can provide significant returns on investment when they are applied to narrow but difficult business problems and are supported by adequate amounts of relevant, quality data. Many such projects start with high hopes but get derailed due to fundamental problems with source data, which were unknown to the organization before embarking on the work. Without some careful analysis before beginning work, these defects can crop up as blockers surprisingly late in the AI/ML development process. Josh Powers proposes an eyes-wide-open approach to specific data readiness assessment that establishes enforceable go/no-go criteria for any such effort. He will focus on hands-on assessment of datasets and dive into non-traditional data engineering practices such as synthetic dataset creation, ontologies, and time series analysis. Workshop attendees will leave with specific, measurable characteristics of their data, which they can apply in the planning, development, and deployment of their AI/ML projects.