by Daniel Lambert (book a 30-minute meeting)

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According to Gartner, it takes on average, organizations 7 months to develop AI initiatives, with 47% of the surveyed companies taking between 6 to 24 months from prototype to production. Delivering AI at scale includes addressing several key challenges to make sure that artificial intelligence (AI) systems can be effectively deployed and managed across a wide range of applications and use cases. Chirag Dekate talked about this subject at the 2023 Gartner IT Symposium Xpo event in Orlando Florida. Here are some of the key principles and practices that can help a company like yours successfully deliver AI at scale:

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AI Talent and Skill Development:

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• Your organization needs to build a valuable AI team. As shown in Figure 1 below, it should include the following roles: AI Architect, Engineers, Model Validator, Business Owner, Business Expert, Data Engineer, Data Scientist, and AI Expert.

• Trying to hire most of your team members outside your organization will most probably take too long. Using trained internal resources for most of these roles will accelerate delivery significantly.

• Invest in training and upskilling your AI team to keep up with the latest developments in the field.

• Prepare for change and train all users of your AI applications while moving from pilot to production.

Data Management:

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• High-quality data is the foundation of AI. It’s often a tortuous task to find it and make it of sufficient quality to successfully train your AI application. Collect, store, and manage data effectively to ensure it’s accurate, relevant, and accessible for training your AI models.

• Implement data governance and security measures to protect sensitive information.

Scalable Infrastructure:

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• Invest in a robust, scalable, secure, and cloud-based computing infrastructure to support AI workloads. Cloud services like AWS, Azure, and Google Cloud provide the necessary resources.

• Leverage containers and orchestration tools like Docker and Kubernetes to manage AI deployments efficiently.

Model Development and Training:

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• Develop and train AI models using best practices in machine learning, deep learning, and data science.

• Use frameworks and libraries for model development.

• Implement automated model training pipelines to streamline the process.

Deployment and Monitoring:

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• Deploy AI models as microservices or serverless functions to make them easily accessible to applications and users.

• Select the right AI data output drift indicators before your deployment.

• Implement and set up monitoring and logging based on your selected drift indicators to track your model performance and system health.

• Set up alert mechanisms to respond to issues in real time, as shown in Figure 2 below.