Is It Time To Ramp Up Your Automated AI Deployment Approach?

It can be challenging to decide which digital transformation initiatives to tackle by leveraging AI/ML solutions. Defining the business case first should always come before selecting a technology.

Getting Started

It can be challenging to decide which digital transformation initiatives to tackle by leveraging AI/ML solutions. The implementation of production-ready AI/ML models deployed to the edge of your equipment can compress the time-to-value solution delivery lifecycle by selecting the right platform, but filtering past the AI vendor marketplace of smoke & mirrors can be daunting.

Common problems in deploying AI/ML solutions within an organization:

• Executive-level AI adoption driven from the C-Suite down to plant teams without end-user buy-in
• Deploying AI is resource-intensive and extremely costly
• Lacking operational team member and SME input to the initial modeling process
• Lengthy time-to-value solution delivery lifecycles in deploying a single AI model
• Stuck in Pilot purgatory with no visibility of scaling solution past the initial PoC stage

What To Look For:

1. Does the AI platform enable Hybrid architecture deployments leveraging both the cloud and the edge?
2. How is data governance addressed? Who owns the data and developed models?
3. Plant floor Enablement for real-time edge connectivity to field devices and OT automation systems?
4. How much ETL resources will be required to deploy our models? Who is responsible for the CI/CD pipelines?
5. How much ML and custom data science resources are required? Where do we access this talent pool? Is there Native integration made readily available?

How to evaluate different frameworks:

Don’t put the cart before the horse. Defining the business case first should always come before selecting technology. Use the following checklist when evaluating frameworks to partner with the right technology solution to avoid potential future costly technical debt.

• CONNECTIONS: Can the AI platform connect and ingest any data from 3rd party applications
• PRICING: What does the pricing model look like? Is it transparent?
• GOVERNANCE: Who owns the models, and will they be proprietary to our organization?
• THE EDGE: Can the platform be configured to self-adjusted local assets at the edge to enable real-time monitoring and closed-loop AI?
• SCALABILITY: Are the deployed production models and templates transferable across replica assets and sites within the organization?
• USER FRIENDLY: How user-friendly is it? Is the experience tailored for engineering-minded end-users as opposed to data scientists?
• BIG DATA: Is it equipped to handle large data and small data volumes?