Continuous AI

Continuous AI service model is designed to emphasize the necessity of iterative progress not only in model development and deployment but also model incubation phases.

Lifecycle of a Model Development is not a Linear Process

Continuous AI service model is designed to emphasize the necessity of iterative progress not only in model development and deployment but also model incubation phases. As approach directly suggests, business understanding, data understanding, data preparation, and model training steps can be revisited. So, process can be disrupted by considering the changing data, concept, or business rules.
KoçDigital Continuous AI team provides perpetual proactive service to deal with the inevitable decline in project deliverables and value (accuracy, explanatory power etc.).

Why Do Machine Learning Models Die

Very high budgets are spent on AI and analytics projects by companies each year, and these budgets are expected to grow in the near future. Despite the high budgets spent, very few analytical projects can run smoothly and continuously.

Various changes over time, explained below, are among the most important reasons why AI projects have such a low success rate despite the excess of resources spent.

• Concept Drift (reduced ability of existing features to explain the predicted value)
• Data Drift (change in distribution of existing features)
• Algorithm Drift (assumptions becoming inadequate, change in business needs)

You can find more details of drifting in Drifting Effects on AI Models Why Continuous AI is a Must? article.

Our Solution Approach

Our Continuous AI methodology deals with these problems with 3 key components.

a. Proactive System Monitoring
Establishing an effective notification and warning system prior to deployment of AI and Machine learning models is a must. Health of the model’s life cycle is directly proportional to the power of monitoring and intervention processes

b. Continuous Model Improvement
Models are analyzed regularly for the possible enhancement points, and when an opportunity emerges; algorithm changes and tests of the related revision take place throughout this phase. After the validation of the results, related improvement is deployed to the production environment with the corresponding project documents and updates; and project stakeholders are notified through planned communication channels.

c. High Skilled Experts
All these processes are executed flawlessly by the experts in Analytics Consultant, Machine Learning Engineer, and System Engineer roles in order to ensure continuity of AI model lifecycle.

You can reach our comprehensive solution methodology in our article A Manifest for Continuous AI

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