mlops-specialiazation-duke

The MLOps Specialization from Duke University is a comprehensive 4-course series designed for programmers, data scientists, and researchers to master machine learning operations. You'll learn Python and Rust fundamentals, leverage GitHub Copilot for productivity, and build production-ready ML systems using Amazon SageMaker, Azure ML, and MLflow. The curriculum covers designing full MLOps pipelines, fine-tuning Large Language Models with Hugging Face, and deploying containerized models in ONNX format. Hands-on projects include automating data preprocessing, building ML/AI solutions with AI pair programming, and implementing GPU-accelerated tasks for improved performance.

1. **Define Metrics**: Replace [SPECIFIC_METRICS] with the key performance indicators (KPIs) for your model (e.g., latency, accuracy, resource usage). Use Duke’s MLOps curriculum to identify which metrics are critical for your use case. 2. **Set Thresholds**: Customize [CRITICAL_FAILURES] based on your model’s SLA (e.g., latency < 200ms, accuracy > 95%). Refer to Azure ML/SageMaker’s documentation for platform-specific thresholds. 3. **Deploy Exporter**: Build the Rust exporter using `cargo build --release` and deploy it alongside your model (e.g., as a sidecar in Kubernetes). Test locally with `curl http://localhost:9090/metrics`. 4. **Configure Prometheus**: Add the exporter’s endpoint to Prometheus’s `prometheus.yml` under `scrape_configs`. Reload Prometheus with `kill -HUP <pid>`. 5. **Visualize & Alert**: Import the provided Grafana dashboard JSON into Grafana Cloud or self-hosted Grafana. Link Alertmanager to your email/SMS/pagerduty for notifications. **Tips**: Use Duke’s MLOps labs to practice deploying Prometheus in a sandbox environment. For Rust exporters, leverage the `prometheus` crate’s examples to handle authentication and retries robustly.