Considerations for full implementation

1. Considerations for full implementation

There are certain components that require more attention to scale the POC to a full implementation.

2. Scale and sustain an AI solution

Along with the data, infrastructure, and talent - management and governance practices are key to the sustainability of an AI solution. There are three primary areas: DevOps,

3. Scale and sustain an AI solution

MLOps,

4. Scale and sustain an AI solution

and Compliance, Privacy, and Security.

5. DevOps

DevOps is a set of practices which enable better collaboration between teams, integration into systems, and deployment. This is achieved through automation of processes such as testing, building, deploying, and infrastructure provisioning. DevOps for an AI solution is important to ensure the structure in which the AI model will sit is continuously improved and monitored for performance degradation.

6. MLOps

MLOps is similar to DevOps in that it aims to operationalize the development and deployment of a system. MLOps focuses on the data and algorithms of an ML or AI solution. Therefore, this practice is more essential in a full AI solution because it will impact the core of the system - the model. The key components of MLOps are - data management,

7. MLOps

model retraining and logging,

8. MLOps

Continous integration and delivery, something directly borrowed from DevOps that involves automated and continuous AI model development and deployment, including testing and validation.

9. MLOps

Next is monitoring,

10. MLOps

and maintenance. We won’t cover details of all these components in this course. Each contributes to a solid practice for ensuring the AI solution continues to drive business value. Likewise, it allows for horizontal scaling of AI solutions in a business as new use cases are identified.

11. MLOps - Monitoring the AI

MLOps includes mechanisms for monitoring the AI model, ensuring it continues to perform as expected. Expectations will be based on the baseline performance of the model, for example accuracy of predictions, when it was first deployed. If this performance declines, it is known as model drift. This can occur for different reasons such as the relationship of the data and patterns change or the data itself changes. Both are bad for the AI solution and can negatively impact decision making based on outputs. Incorporating monitoring with an MLOps practice safeguards against this.

12. Compliance, privacy, and security

Compliance, privacy, and security are important during a POC, though with the small user base, it is possible to achieve with minimal measures. They are essential to scaling an AI solution as it will protect user's rights, build trust, and mitigate risks. It is also part of the Responsible AI framework mentioned at the beginning of this course. Here are some major components to think about.

13. Legal and regulatory requirements

First, legal and regulatory requirements. In today’s world, it is common for a business to have a global user base. Therefore, it is important to build the AI solution to meet regulatory requirements in each location. This is especially true if handling sensitive or personal data. Adhering to these legal obligations will help you avoid penalties and legal consequences. Consult legal counsel to ensure adherence to legal obligations.

14. Security

Security is essential in today’s digital world. The AI solution should include processes to ensure the data, infrastructure, and product are secure from bad actors. Likewise, authentication and authorization services are required to make sure only verified users can interact with the solution.

15. Risk management and mitigation

Risk management and mitigation. Non-compliance with regulations and protections and breaches to the data or system can be extremely damaging to the user and business. A full-scale solution should instrument processes to audit the solution. This could be legal examinations to ensure compliance, risk assessments to identify areas for improvement, or even simulated attacks to determine weak areas of the infrastructure.

16. Let's practice!

With strategies for these areas, we're on our way to a successful, full-scale AI solution!

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