Start: Q2 2024
Publication: Q4 2024
Region: Global
As companies expand the role of analytics and artificial intelligence in their business, they collide with a familiar problem: old, bad data and changing ML outputs. Overwhelmed data teams struggle to reliably deliver the high-quality inputs that modern BI and AI/ML projects require. MLOps and DataOps programs help by applying principles of DevOps, agile software development and total quality management to ML and data pipelines. Both Ops disciplines comprises testing, continuous integration and deployment (CI/CD), pipeline automation and data respecitvely ML observability.
It will examine adoption drivers, obstacles, challenges and best practices for success. By comparing best-in-class and laggard adopters, readers will learn proven techniques for optimizing ML and data pipelines – and the business results they generate.
Key questions:
- Common issues with data quality, ML and pipeline performance
- The scale and scope of MLOps, DataOps and data observability programs worldwide
- Why enterprises do and don’t adopt these programs
- What business benefits they achieve
- How MLOps, DataOps and data observability can fail
- Typical use cases and lessons learned from them