How to Architect and Deliver AI at Scale
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:
AI Talent and Skill Development:
- 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.
- 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.
- 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:
- 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:
- 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.
Automation and DevOps:
- Apply DevOps practices to AI to automate deployment, scaling, and monitoring to shorten the time from prototype to production.
- Version control for AI models and pipelines is essential for tracking changes and rolling back in case of issues.
Reasoning and Transparency:
- Ensure AI models are explainable and transparent, especially in critical use cases.
- Address ethical and bias concerns by monitoring and mitigating bias in AI models.
Compliance and Regulation:
- Stay informed about the regulatory landscape in your industry, such as GDPR, HIPAA, or industry-specific regulations.
- Ensure compliance with data protection, privacy, and security standards.
- Provide user-friendly interfaces for data scientists and non-technical stakeholders to interact with AI models, such as dashboards and APIs.
- Make AI outputs accessible and interpretable for end-users.
- Continuously update and retrain AI models to keep them relevant and accurate.
- Collect user feedback and use it to refine and enhance AI solutions.
- Prioritize AI system security to protect against threats and vulnerabilities.
- Regularly update and strengthen AI software components to mitigate security risks.
- Monitor and optimize AI infrastructure and cloud costs to ensure cost-effectiveness.
- Delivering AI at scale is a complex and ongoing process that requires a holistic approach, careful planning, and a commitment to maintaining the quality, security, and efficiency of AI solutions. It’s important to tailor these principles to the specific needs and goals of your organization and adapt them as the AI landscape evolves.