To carry out DevOps for data science, you need to extend the ideas of DevOps to be compatible with the processes of data science and machine learning (ML). In this 12-video course, learners explore the concepts behind integrating data and DevOps. Begin by looking at applications of DevOps for data science and ML. Then examine topological considerations for data science and DevOps. This leads into applying the high-level organizational and cultural strategies for data science with DevOps, and taking a look at day-to-day tasks of DevOps for data science. Examine the technological risks and uncertainties when implementing DevOps for data science and scaling approaches to data science in terms of DevOps computing elements. Learn how DevOps can improve communication for data science workflows and how it can also help overcome ad hoc approaches to data science. The considerations for ETL (Extract, Transform, and Load) pipeline workload improvements through DevOps and the microservice approach to ML are also covered. The exercise involves creating a diagram of data science infrastructure.
DevOps for Data Scientists: Data DevOps Concepts