Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers
By: Jasper Stone , Raj Patel , Farbod Ghiasi and more
Potential Business Impact:
Makes AI work better and easier for everyone.
The adoption of Machine Learning Operations (MLOps) enables automation and reliable model deployments across industries. However, differing MLOps lifecycle frameworks and maturity models proposed by industry, academia, and organizations have led to confusion regarding standard adoption practices. This paper introduces a unified MLOps lifecycle framework, further incorporating Large Language Model Operations (LLMOps), to address this gap. Additionally, we outlines key roles, tools, and costs associated with MLOps adoption at various maturity levels. By providing a standardized framework, we aim to help organizations clearly define and allocate the resources needed to implement MLOps effectively.
Similar Papers
Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
Cryptography and Security
Protects smart computer programs from being tricked.
Embedding the MLOps Lifecycle into OT Reference Models
Machine Learning (CS)
Helps factories use smart computer programs safely.
Operationalizing AI: Empirical Evidence on MLOps Practices, User Satisfaction, and Organizational Context
Software Engineering
Makes building smart computer programs easier.