Central Time CT
As enterprise demand for advanced analytics, machine learning, and AI grows, data and analytics professionals struggle to keep pace. Consistent delivery of modern analytics requires an approach that is agile, repeatable, and scalable. In this course, you will learn how the DataOps and MLOps methods for delivering data and analytics meet this challenge, providing a mechanism for rapid and repeatable delivery of data and analytics that is scalable and manageable, while also living up to business expectations.
DataOps is a process-focused and automated methodology for delivering data for machine learning and AI that concentrates on reducing cycle time and improving the quality of advanced analytics deliverables. DataOps builds on the concepts of DevOps, continuous integration and delivery (CI/CD), and agile. These concepts support quick and efficient software delivery, but analytics is more than software—it is also about the delivery of insights. The DataOps approach delivers consistent and meaningful data which means automating the data lifecycle of acquisition, understanding, integration, transformation, and deployment.
Machine learning operations (MLOps) is a process-focused and automated methodology for delivering advanced analytics. Building on the data management foundations of DataOps, MLOps provides end-to-end processes to develop, build, test, and automate machine learning and AI models. MLOps practices enable organizations to adopt to decrease model time to market and create reproducible capabilities.
In this course, you will learn the principles of DataOps and MLOps, how these processes work together, and how they differ from conventional software development. You will learn best practices that enable repeatable delivery and operations processes that can scale as business demands grow. Learn how to start taking data and analytics delivery to the next level in your business.
You Will Learn
- Definition, scope, and components of DataOps and MLOps
- How these approaches apply agile and DevOps principles to data and analytics
- The key relationships between DataOps and MLOps
- Principles of continuous integration/continuous delivery (CI/CD) for data management
- How machine learning models and training sets are incorporated into the CI/CD process
- How these approaches reduce technical debt
- The central role of automation
- Best practices and how to get started
- Program managers for data and analytics
- Project leads
- Data scientists
- Data engineers
- ML engineers
- Business stakeholders
- Analytics managers