Considerations for full implementation

Implementing AI Solutions in Business

Jacob H. Marquez

Data Scientist

Scale and sustain an AI solution

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Scale and sustain an AI solution

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Scale and sustain an AI solution

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DevOps

 

• "Development Operations"
• Better collaboration, integration, deployment
• Automation of processes
• Continuous improvement and monitoring

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MLOps

 

• "Machine Learning Operations"
• Similar to DevOps
• Operationalize processes for data and algorithms

MLOps

 

• "Machine Learning Operations"
• Similar to DevOps
• Operationalize processes for data and algorithms

MLOps

 

• "Machine Learning Operations"
• Similar to DevOps
• Operationalize processes for data and algorithms

MLOps

 

• "Machine Learning Operations"
• Similar to DevOps
• Operationalize processes for data and algorithms

MLOps

 

• "Machine Learning Operations"
• Similar to DevOps
• Operationalize processes for data and algorithms
• Allows for horizontal scaling

MLOps - Monitoring the AI

 

• Compare model performance to baseline
• Example: accuracy of predictions during first month of deployment
• Model Drift when performance declines

• Negatively impact decision-making

Compliance, privacy, and security

 

• Important during POC
• Can achieve with minimal measures
• Essential for scaling to full implementation
• Part of Responsible AI

 

Legal and regulatory requirements

 

• Meet regulatory requirements in EACH location
• Especially with sensitive and personal data
• Avoid penalties and legal consequences

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Security

 

• Ensure data, infrastructure, and product are secure
• Authentication
• Authorization

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Risk management and mitigation

 

• Avoid damage to the solution, customer, and business
• Audit the solution
• Legal examinations
• Risk assessments
• Simulations

Let's practice!

Implementing AI Solutions in Business