Composable, Reusable, Continuously Learning Analytic Module Architecture
Figure 6.2 shows a state-of-the-art (as of 2020) analytic module architecture. The architecture is comprised of numerous open source components (MLflow, Seldon Core, Jupyter Notebook, Python, Spark ML, TensorFlow, and so on) built upon a Kubernetes and Docker foundation to facilitate the reuse and portability of the analytic modules across cloud hyperscalers (Amazon Web Services, Google Cloud Platform, Microsoft Azure) as well as on-premises and within embedded product environments.
Figure 6.2: Composable, Reusable Analytic Module Architecture
These composable, reusable, continuously learning analytic modules have the following capabilities:
- Pre-defined data input definitions and data dictionary (so it knows what type of data it is ingesting, regardless of the origin of the source system)
- Pre-defined data integration and data transformation algorithms to cleanse, align, and normalize...
