Scale AI model governance with the AI Factory framework
AI and machine learning projects are on the rise. According to Gartner, 48% of CIOs and tech executives have deployed, or plan to deploy, an AI/ML project in 2022. It’s also estimated that 50% of IT leaders will struggle to drive their AI initiatives from Proof of Concept (PoC) to production through 2023.
Challenges moving AI/ML initiatives from PoC to production
What causes the gap between PoC and AI/ML model implementation?
IT and business leaders often cite challenges relating to security, privacy, integration, and data complexity as the key barriers to deploying AI/ML models in production. It is often due to governance frameworks not being shared across an organization to ensure compliance and maintainability – if a framework exists at all.
“At some point, your proof-of-concept is likely to turn into an actual product, and then your governance efforts will be playing catch-up,” writes Mike Loukides is an O’Reilly report. “It’s even more dangerous when you’re relying on AI applications in production. Without formalizing some kind of AI governance, you’re less likely to know when models are becoming stale, when results are biased, or when data has been collected improperly.”
AI models require constant attention in production to achieve scalability, maintainability, and governance. To do that, organizations need a strong MLOps foundation.
Leveraging MLOps at scale
In one survey, Deloitte found that organizations that strongly followed an MLOps methodology were…
Organizations following an MLOps methodology also gain a clear advantage in time to deployment. McKinsey found that companies without a formalized MLOps process often took 9 months to implement a model. In comparison, companies applying MLOps could deploy models in 2 to 12 weeks!
The secret? By applying MLOps practices, these companies were able to create a “factory” approach for repeatable and scalable AI/ML model implementation. Their engineers weren’t building everything from scratch. They could pull from a library of reusable components, automate processes, and ensure compliance and governance throughout the organization.
Luckily, you can also take this approach with Neal Analytics’ AI Factory Framework.
Neal Analytics AI Factory Framework
The Neal Analytics AI Factory Framework is a cloud-based MLOps framework that provides organizations with the foundation to deliver Data Science, Machine Learning, and AI projects at scale. It offers enterprise-level reusability, security, integration, and governance.
Simply put, AI Factory helps customers scale MLOps, centralize governance, and accelerate time to deployment.
Key benefits of the AI Factory
By leveraging reusable and standardized artifacts, automated pipelines, and governance solutions, our AI Factory framework reduces duplicate effort and upskilling needs between teams and projects.
Neal AI Factory Framework benefits
Customers leveraging the Neal Analytics AI Factory Framework can take advantage of Neal’s AI engineering best practices to accelerate deployment and ensure model governance at scale.
AI Factory also helps businesses:
- Make the entire end-to-end lifecycle more repeatable, governable, safer, and faster
- Shorten planning and development with accelerated time to deployment
- Streamline operational, security, and governance processes
- Reduce development risks & improve model quality
- Reduce team’s upskilling needs
- Achieve higher success rates & ROI
Over the last decade, Neal Analytics has helped many customers build and execute their AI governance strategy. We distilled this experience and the derived best practices in this framework, to help deliver customers’ AI/ML initiatives at scale.
Want to set up your own AI Factory Framework? Contact us to get in touch with one of our experts!