Whenever we suggest any innovative ideas and concepts to business, the primordial question is the need for those innovations in the context of the particular business. A similar question is very much applicable in the case of MLOps as well. When we ask why does any business needs MLOps, pondering on the following key pointers will help in understanding the pressing need for MLOps.
The current process of managing models is mostly manual and the need for automation is vital. The data drift is unavoidable and the same has to be efficiently monitored and controlled. On a similar line, efficiently tracking the model Accuracy & Data quality in production, validating the model hypothesis, and efficiently handling the management of downstream models are important.
Businesses need to implement MLOps practice and see the impact it delivers on current solutions in terms of drift, understanding the models, measuring accuracy, standardized deployments, streamlined Syndication process to publish model scores to vendor systems.
Challenges
- A scheduled way of tracking model errors/effectiveness is being missed out in many cases
- Less visibility into production performance
- No visibility for sales/marketing into the ROI the model generates ($ impact)
- Delay in publishing model output insights despite (Schedule/communication gap)
- Identification of error is late for downstream consumption
Solution Approach:
- Identify the gap in the process of model development, deployment, testing, consumption during the discovery phase
- Design & recommend a framework to streamline the current processes across
- Model development
- Deployment
- Monitoring
- Model score publishing
- Feature Store & Feedback Loop
Benefits:
- Automated pipeline to periodically update backdated campaign effectiveness and model errors
- Streamlined & automated pipeline designed, reducing the manual interventions and multi-team dependency
- Bridge the gap between sales and marketing teams by bringing in automated ROI calculation and the actual $ impact/sales
- Visualize the metrics along with drift on the dashboard and validate retraining rules
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