IDC PeerScape: Best Practices for Overcoming Enterprise Machine Learning Operations Challenges to Fortify Enterprise Intelligence
This IDC PeerScape discusses best practices for overcoming enterprise MLOps challenges."MLOps is a team sport. It requires collaboration and collective learning among data engineers, data scientists, ML engineers, and business experts with different skills and tools," said Kathy Lange, research director, AI Software research at IDC. "Standardizing and automating MLOps processes are vital for delivering insights at scale across the enterprise and delivering value from AI investments. MLOps can help organizations overcome short-term skills shortages while they reskill, build critical talent, and improve AI maturity."
Please Note: Extended description available upon request.
- IDC PeerScape Figure
- Executive Summary
- Peer Insights
- Practice 1: Build an Integrated Team with a Multidisciplinary Skill Set
- Challenge
- Example
- Retail Pharmacy
- Guidance
- Practice 2: Create a Standardized Machine Learning Software Stack and an Automated Deployment Process
- Challenge
- Example
- Financial Services Firm
- Guidance
- Practice 3: Take a Phased Approach to Deliver AI Value Using MLOps; Start with a Solid Data Foundation
- Challenge
- Example
- Global Automotive Distributor
- Guidance
- Learn More
- Related Research
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