The reference for
Artificial Intelligence
initiative and implementation
Artificial Intelligence Maturity Model
.
AI Maturity Model
Welcome to the
The AI Maturity Model defines the disciplines and its different levels of sophistication, needed for the proper use of Artificial Intelligence in any organization.
Each discipline has clear maturity definitions (from 1 to 5) that will allow organizations to continuously assess and evolve its disciplines
The disciplines of the AI Maturity Model
Data
Data is the most fundamental discipline of the AI MM. Here we define the level of organization of your data and the readiness for AI use
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MLOps
The Machine Learning Ops defines how well your process for a Machine Learning operations are set as well the linked practices
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DevOps
The Development Ops reflects the maturity of your software development practices. AI implementation requires the software development practices to be in place
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Strategy
This discipline defines your Artificial Intelligence strategy and how it will impact your organization
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People
Your team must be ready for an AI implementation. Understanding the knowledge of your team around AI will help you trace plans for improving your teams capabilities
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Models
You need to understand which models will be used by your organization, how they will be trained and how the model will evolve
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AI Governance
The governance of your AI practice will dictate if your organization has a plan to evolve and make each discipline more mature
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Security
Your data, models and generated information must be secured. You might choose to used closed models or open and self hosted models. Either way, the security of your data must be assessed
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Applications
The use of AI passes through applications that can properly use your models to generate useful information
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Infrastructure
The IT Infrastructure definition and foundation is the basis for a AI implementation
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