The Importance Of MLOps in AI/ML

By Pranav kumar Chaudhary on

April 1, 2024

Importance Of MLOps in AI and ML MLOps 0 (Manual): This is sufficient for the scenario where a model is rarely changed, retrained, or fine-tuned. The process is manual and controlled by scripts. However, this will become challenging as soon as model deployment becomes frequent due to changes in business requirements or data.

MLOps 1 (ML Pipeline Automation): This allows for rapid experimentation by automating the ML pipeline. This encompasses automated data validation, feature stores, metadata management, and ML pipeline triggers.

MLOps 2 (CI/CD Pipeline Automation): In case of a rapid development environment where training/re-training is required frequently (daily) and inference on a very large scale, this level of automation is required. This level of automation ensures end-to-end model delivery, management, control, and monitoring with various triggers in place.

Use Case

Let’s assume a chatbot scenario for a large e-commerce company. The volume of data and inquiries they get daily is huge. The data changes frequently and leads to new opportunities for the bot to learn and revert accordingly. Training a model on a snapshot of data could lead to a stale model after some days, and this required frequent training.

To cope with such a huge demand for retraining, fine-tuning, experimentation, testing, and monitoring, MLOps is required. MLOps will reduce the burden and increase the pace of innovation by a huge margin.

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