Step 6: Set Up Prometheus and Grafana for Monitoring

To monitor the performance, resource usage, and latency of your translation system, you can use Prometheus and Grafana.

1. Install Prometheus and Grafana:

   • Use Helm (a Kubernetes package manager) to install Prometheus and Grafana on your Kubernetes cluster:

     bash
     Copy code
     helm repo add prometheus-community <https://prometheus-community.github.io/helm-charts>
     helm install prometheus prometheus-community/prometheus
     helm install grafana grafana/grafana
     
     

2. Configure Prometheus to Scrape Metrics:

   • Set up Prometheus to monitor metrics from your Kafka brokers, FastAPI translation service, and Kubernetes pods. You may need to expose the FastAPI service metrics endpoint (/metrics) for Prometheus to scrape.

3. Create Grafana Dashboards:

   • Use Grafana to visualize metrics such as:
     • Translation latency (time taken for translations)
     • Throughput (number of messages translated per second)
     • CPU and Memory Usage (Kubernetes pod resources)
   • Set up alerts for thresholds, like high latency or memory usage, to proactively address issues.

Step 7: Integrate Weights & Biases for Drift Detection and Logging

To maintain translation quality, Weights & Biases (W&B) will be used for drift detection, logging model performance metrics, and triggering automated retraining.

1. Set Up a W&B Project:
   • Create a project in W&B to log translation quality metrics (e.g., BLEU scores, KL Divergence) and monitor drift.
2. Implement Drift Detection Logic:
   • Calculate KL Divergence periodically to track shifts in translation data distribution.
   • When drift is detected (KL Divergence exceeds a threshold), W&B should trigger a retraining alert.
3. Log Metrics in W&B:
   • Track BLEU scores for translation quality. Update these scores after each batch of translations to ensure the model’s performance remains high.
4. Trigger Retraining Pipeline:
   • Set up a Kubernetes CronJob that activates an automated retraining pipeline whenever W&B detects drift. This retraining pipeline will fine-tune your Hugging Face model on recent data.