MLOps — Where ML Meets DevOps
Description
MLOps — Where ML Meets DevOps
This IDC Perspective draws parallels between MLOps and DevOps approaches. As customers are moving more models from experimentation to production, they need scalable ways to collaborate, operate, and operationalize machine learning models. To leverage the tremendous opportunities that this provides, IDC recommends leveraging MLOps methodologies, building upon DevOps processes to improve collaboration between data scientists and operational engineers, automate, and accelerate model velocity."Top challenges that customers face with implementing AI/ML initiatives in production include lack of expertise, cost, and lack of automation," said Sriram Subramanian, research director, AI and Automation Software research at IDC. "MLOps capabilities enable customers to overcome these challenges by improving collaboration between data scientists, application developers, and operational engineers; automating end-to-end model life-cycle management; and increasing model velocity."
Please Note: Extended description available upon request.
This IDC Perspective draws parallels between MLOps and DevOps approaches. As customers are moving more models from experimentation to production, they need scalable ways to collaborate, operate, and operationalize machine learning models. To leverage the tremendous opportunities that this provides, IDC recommends leveraging MLOps methodologies, building upon DevOps processes to improve collaboration between data scientists and operational engineers, automate, and accelerate model velocity."Top challenges that customers face with implementing AI/ML initiatives in production include lack of expertise, cost, and lack of automation," said Sriram Subramanian, research director, AI and Automation Software research at IDC. "MLOps capabilities enable customers to overcome these challenges by improving collaboration between data scientists, application developers, and operational engineers; automating end-to-end model life-cycle management; and increasing model velocity."
Please Note: Extended description available upon request.
Table of Contents
8 Pages
- Executive Snapshot
- Situation Overview
- Overview
- Challenges Implementing AI Solutions
- Lack of Automation
- Cost
- Lack of Expertise
- What Is MLOps?
- Model Serving
- Model Registry
- Model Tracking
- Model Monitoring
- ML Pipeline and MLOps
- DevOps at a Glance
- Machine Learning Meets DevOps
- Advice for the Technology Buyer
- Treat Models as Source Code
- Plan for Scale
- Build Upon DevOps Processes
- Learn More
- Related Research
- Synopsis
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