Machine Learning models impact to a business is not in doubt, but providing the the right environment to maximse the value-impact, on a ongoing, repetable and consistent manor, can be a challenge.

• Inconsistent deployment and monitoring of models across and organization.
• Slow model deployment cycle - falling behind.
• Out of date or unrefreshed models - not reliable / declining insights.
• Investment in ML & Technology is high - ROI is not matched.
• Centralized model performance management - poor or not in existance.
• Data Strategy - poor data quality, poor data pipelines, poor models, difficult workflows.
• Model performance is not always measured or is manual.

All the above can impact the execution and value of Gen AI to a business.

Where to start?

Assessing the maturity of an MLOps environment involves evaluating various aspects of the system, including: automation, scalability, reliability, and more. These are technical variables; however we also focus on processes and people to ensure a comprehensive understanding of your environment and actionable outcomes.

The following benefits can then be achieved:

• Industrialised ML accelerating speed to value
• Focus on ML innovation and not infrastructure
• Reduced infrastructure cost
• Accelerate Customer Data Integration
• Optimised Operating Model
• Customer Data Privacy