Course Objective & Scope
An inference endpoint can return HTTP 200 in under 30 ms while silently producing garbage predictions, because traditional APM tools only see server health—not feature payloads, prediction distributions or downstream business outcomes. This course covers end-to-end observability architectures for production machine learning services.
Collecting latency, queue and GPU telemetry with OpenTelemetry, Prometheus and Grafana; non-blocking logging of features and predictions (Kafka/Parquet); joining delayed ground-truth labels back to predictions; building slice-level performance dashboards and writing incident response runbooks are practised hands-on. Participants build a monitoring pipeline and Grafana/Evidently dashboard for their own services.
Who Should Attend?
- MLOps and SRE engineers responsible for the reliability and quality of production AI services
- Data scientists who want visibility into how their models perform on live traffic
- Platform teams extending existing observability stacks (Prometheus, Grafana, OpenTelemetry) to ML workloads
- Prerequisite: Python, basic SQL and familiarity with REST APIs and containers
Key Takeaways
- Unify operational metrics (p99 latency, error rates, GPU/VRAM) with ML quality metrics in a single observability stack
- Log input features, model versions and prediction probabilities asynchronously without adding tail latency
- Build a join pipeline that reconciles delayed ground-truth outcomes with historical predictions via request IDs
- Track confidence distributions, null/out-of-range feature rates and business proxy KPIs when ground truth is delayed
- Design multi-tier alerting rules and incident runbooks (fallback, traffic shift, rollback) that avoid alert fatigue
Curriculum
Why Traditional APM Falls Short: The ML Observability Stack
- Anatomy of silent ML failures: broken upstream feature joins, unit changes and calibration decay
- The four monitoring layers: Infrastructure/GPU, Service/API, Data/Prediction signals and Ground-Truth Business KPIs
System and Service Telemetry with OpenTelemetry, Prometheus and Grafana
- Instrumenting FastAPI/Triton endpoints with latency histograms, batch sizes and model_version labels
- Using OpenTelemetry spans to break down latency across feature retrieval, preprocessing and model execution
High-Throughput Prediction Logging and Ground-Truth Reconciliation
- Non-blocking asynchronous logging of input features, predictions and confidence scores to Kafka/S3
- Smart sampling strategies (low-confidence and stratified sampling) and PII redaction for high-QPS endpoints
Live Accuracy Tracking and Handling Delayed Feedback
- Joining outcomes that arrive days or weeks later (churn, credit default, fraud) back to inference logs
- Proxy metrics, confidence-calibration tracking and cohort/slice monitoring when ground truth is unavailable
Dashboards, Alerting Hygiene and ML Incident Runbooks
- Building shared Grafana and Evidently dashboards serving both SREs and data scientists
- Tuning alert thresholds and executing structured incident runbooks (circuit breakers, rule fallbacks, model rollbacks)
Other Courses in This Category
-
The maths of covariate, label and concept drift; PSI, KS, Wasserstein and MMD tests; label-free performance estimation with Evidently/NannyML and retraining strategies, for data scientists and MLOps teams.
- Live Course
- 5 modules
-
Feature Store architectures preventing training-serving skew and time leakage: offline (Parquet/Snowflake/BigQuery) and online (Redis) stores with Feast, point-in-time joins and streaming features, for data and MLOps teams.
- Live Course
- 5 modules
-
Pre-deployment fairness (Fairlearn), explainability (SHAP), probability calibration, uncertainty quantification (Conformal Prediction), guardrails, progressive rollouts and kill-switches, for ML and risk teams.
- Live Course
- 5 modules
-
Designing an enterprise AI platform unifying classical ML and GenAI/LLMs: compute/GPU orchestration, data and model layers, AI Gateways, multi-tenant isolation and reference architectures, for architects and platform leads.
- Live Course
- 5 modules
-
Designing an Internal Developer Platform (IDP) for data scientists and AI engineers: Backstage templates, on-demand notebook/GPU workspaces, Golden Path pipelines and self-service deployment, for platform teams.
- Live Course
- 5 modules
-
IAM/RBAC/ABAC for AI workloads, secret management with HashiCorp Vault and External Secrets, Policy-as-Code with OPA/Kyverno, supply-chain security (pickle/images) and network isolation, for platform and security teams.
- Live Course
- 5 modules
Get in touch
