Production-Ready Observability Platform for AI Systems

7 min readNov 3, 2023

Building robust and reliable AI systems requires going beyond model development to address challenges like data dependencies, framework intricacies, and black-box behavior.

In this blog, I will cover best practices for observability across the full AI system lifecycle — from training to production. We will demonstrate key monitoring, logging, tracing techniques using real-world examples and open source tools.

By the end, you’ll understand how to craft an observable AI platform providing visibility, alerting, and accelerated iteration.

Challenges of Observability in AI Systems

AI model development garners lots of attention. But models are only one piece of operationalized AI systems. Additional components include:

Data collection pipelines
Cloud infrastructure
Frameworks like TensorFlow and PyTorch
Model deployment architectures
Application integration

Monitoring and debugging these complex, interconnected systems brings new challenges including:

Data dependencies — Is data missing or changing unexpectedly?
Blackbox models — Can’t easily trace model internals during inference
Framework intricacies — Are TensorFlow or PyTorch behaving properly?
Continuous retraining — How to track ongoing model changes?
ML technical debt — Managing regressions and drift
Causal links — Which component impacted the model output?

Lack of observability into these areas leads to opaque systems prone to outages and performance issues in production.

Let’s explore techniques to address these challenges and enhance visibility.

Planning AI System Telemetry

The first step is identifying key signals to monitor across components. Metrics, logs, and traces should provide insights into:

Data health — Volume, distribution, drift, dependencies
Infrastructure — Utilization, saturation, errors
Framework operations — Performance, versions, degradation
Models — Accuracy, precision/recall, explainability
Deployment — Availability, latency, reliability

With a telemetry plan, we can instrument our pipelines, infrastructure, and applications accordingly.

For example, logging data schema changes, tracing inter-service requests, tracking GPU utilization, and exporting model explainability metrics.

The specific signals will vary for each system, but should cover these broader categories.

Next, let’s look at collecting this telemetry using standard interfaces.

Structured Logging

Centralized logging of system events provides a foundation for observability.

For a consistent logging interface across languages and frameworks, we can leverage the structlog library:

import structlog

log = structlog.get_logger()log.info("Training started", epochs=30)

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

private static final Logger log = LoggerFactory.getLogger(App.class);log.info("Prediction requested", model_id="abc123");

structlog outputs timestamped, structured logs accessible across technologies:

{"event": "Training started", "epochs": 30, "time": "2023-01-01T12:00:00Z"}

For managing log data, we can ingest logs into a cloud-based aggregated logging system like Splunk, Datadog, or Elasticsearch.

This provides a single pane of glass for searching, filtering, and correlating events across our AI platform.

Metrics and Tracing

While logs provide discrete events, metrics give quantitative insights into aggregates and distributions.

Traces tie cross-component requests together end-to-end.

For collecting metrics and traces in a vendor-neutral format, we can use OpenTelemetry:

from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader

metrics.set_meter_provider(MeterProvider())
exporter = PeriodicExportingMetricReader()
metrics.get_meter_provider().start_pipeline(meter, exporter, 5) # Export every 5srequests_counter = metrics.Counter("requests_total")
requests_counter.add(1)latency_recorder = metrics.Histogram("request_latency_seconds")
latency_recorder.record(0.3)

This exposes metrics following the Prometheus data model. Traces are enabled similarly via the OpenTelemetry trace SDK.

For analytics, metrics can be scraped by Prometheus and visualized in Grafana. Jaeger provides trace storage and analysis.

Instrumenting critical paths with OpenTelemetry gives cross-component visibility.

Model Monitoring

For production models, we need to monitor metrics like:

Precision, recall, accuracy
Data drift
Prediction distribution changes
Service-level performance (SLA)

We can instrument model serving containers to export these metrics.

For example, using Flask and Prometheus:

from flask import Flask
from prometheus_flask_exporter import PrometheusMetrics

app = Flask(__name__)
metrics = PrometheusMetrics(app)@app.route("/predictions", methods=["POST"])
def predict():
   # prediction logic
   prediction_time.observe(time_taken) 
   return resultprecision = metrics.gauge("precision")  
precision.set(0.95)

This exposes an endpoint for Prometheus to scrape app-level metrics.

Model drift and accuracy can be tracked by comparing with a holdout evaluation set.

For model explainability, we can implement SHAP or LIME and publish artifacts to object storage.

This provides indicators into model reputation and behavior over time.

Monitoring Best Practices

Some best practices for effective monitoring:

Trends over snapshots — Track metric deltas and histories vs one-off data
** leading indicators** — Watch predictive metrics like queue growth
Baseline expected ranges — Set dynamic thresholds to avoid floods during normal usage
Segment dimensions — Slice metrics by region, user type, model version etc.
Pick essential signals — Focus on high-value metrics aligned to priorities
Visualize key flows — Dashboard service dependencies and pathways
Alert judiciously- Avoid excessive alerts by checking multiple criteria

Prioritizing the vital metrics that offer actionable insights. Plot trends rather than isolated data points.

Next, let’s discuss how alerts and incidents enable responding quickly to problems.

Alerting and Incident Management

When issues emerge, we want to be notified immediately so we can mitigate impact.

Alerting rules allow configuring triggers based on metrics and logs:

# Alert if average latency exceeds 500ms over 5 minutes
ALERT HighLatency
  IF avg(rate(request_latency_seconds_sum[5m])) / avg(rate(request_latency_seconds_count[5m])) > 0.5
  FOR 10m
  LABELS {severity="critical"}

We can route these alerts to communication channels like email, Slack, PagerDuty etc.

For collaborating on incidents, Jira can be used to track impacted services, post-mortems, and remediations.

Robust alerting and on-call workflows reduce incident severity through fast responses.

Now let’s look at how we can debug and understand issues.

Distributed Tracing

Tracing distributed requests is crucial for diagnosing multi-component issues.

OpenTelemetry provides this tracing out of the box. For example:

from opentelemetry import trace

tracer = trace.get_tracer("data_pipeline")with tracer.start_as_current_span("ingest") as ingest_span:
   ingest_span.set_attribute("num_records", len(records))
   index_records()with tracer.start_as_current_span("train") as train_span:
  train_span.set_attribute("model_id", "model1") 
  train_model()

This instruments the data ingestion and model training steps, linking spans into an end-to-end trace:

The Jaeger UI enables analyzing trace flows across components to identify culprits.

Correlating tracing IDs logged across systems ties unstructured logs to traces.

Anomaly Detection

For detecting emerging system issues and outliers, unsupervised anomaly detection can be applied to metrics and logs.

For example, an Isolation Forest algorithm can detect significantly long inference times:

from sklearn.ensemble import IsolationForest

latency_logs = load_latency_logs() detector = IsolationForest(contamination='auto')
detector.fit(latency_logs)anomalies = detector.predict(latency_logs)if np.any(anomalies == -1):
  send_alert()

By flagging anomalies, we can catch degrading performance early before it causes outages.

Experiment Tracking

When testing improvements to models, data, and other components, tracking experiments is crucial.

MLflow provides experiment tracking and model registry capabilities:

import mlflow

mlflow.set_experiment("flight_delay_model_v2") mlflow.log_param("label_window", "3_hours")
mlflow.log_metric("mse", 0.25)mlflow.log_model(model, "model")

This allows tracing model changes, parameter differences, and results over time.

Integrating with the logging pipeline provides a single lens into experiment outcomes.

Dashboards

Bringing together metrics, logs, traces, and alerts in dashboards provides a unified operational view.

For example, Grafana can visualize Prometheus metrics

Kibana integrates with Elasticsearch logs

Jaeger displays distributed traces

These dashboards offer visibility into AI systems end-to-end.

Retrospectives

To continuously improve reliability, retrospectives assess what worked well and areas for improvement after incidents.

Blameless post-mortems and retros enable identifying systemic gaps and enhancing resiliency.

Some example retrospective discussion topics:

What metrics or alerts could better predict this issue?
What components or interactions escalated the severity?
How could degraded performance be detected and alerted sooner?
What documentation or runbooks need improvement?
What changes will improve recovery and minimize damage next time?

Implementing retrospective learnings directly into configurations, dashboards, and documentation completes the feedback loop.

Causality Analysis

For complex incidents spanning multiple components, determining root causes is challenging.

Advanced techniques like causal graphs and Bayesian networks can help uncover causal relationships from system telemetry.

For example, analyzing whether data errors resulted in bad predictions or if a service outage caused the data issues.

These techniques move beyond correlation to model probabilistic causal links between system variables.

AI observability current trends:

As AI systems grow in complexity, observability becomes even more crucial. Some key trends to consider:

MLOps adoption — Model deployment pipelines need monitoring and instrumentation. Tracking experiments, data changes, and model performance metrics end-to-end.
Hybrid AI systems — Combining neural networks, knowledge bases, search indices, and rules requires tracing flows across disparate components.
Multimodal models — Models fusing text, speech, vision and other modes need instrumentation of each component. Their interactions multiply debugging challenges.
Federated learning — Training models on decentralized edge devices makes aggregating telemetry harder. New protocols needed to share insights.
Lite deployment — Deploying lightweight models on mobile/IoT results in less local visibility. Inference monitoring needs enhancement.
Automated machine learning — Dynamic model exploration and generation complicates tracking. Metadata on experiments is key.
Ethics and fairness — Understanding model behavior on diverse users and data is important. Instrumentation enables auditing.
Causality — Explaining why certain inputs cause specific model results helps build trust. Causality techniques are still maturing.

Observability will continue growing as a priority as AI systems scale in complexity and distribution.

Open Source Tools Stack for AI Observability Platform:

Many powerful open source options exist for monitoring, logging, tracing, and alerting AI systems.

Metrics and Telemetry

Prometheus — Timeseries metric collection and querying
OpenTelemetry — Vendor-neutral metrics, traces, and logs
Grafana — Visualize and dashboard metrics
Graphite — Timeseries metric database
RED — Redis-based metrics collector

Logging

ELK Stack (Elasticsearch, Logstash, Kibana) — Log aggregation and analysis
Fluentd — Unified logging layer
Splunk — Enterprise log management

Tracing

Jaeger — Distributed tracing storage and UI
Zipkin — Correlate request flows across microservices

Experiment Tracking

MLflow — Machine learning experiment tracker
TensorBoard — Visualize complex ML runs

Alerting

Prometheus Alertmanager — Alert rule engine
PagerDuty — Incident response escalations
Sentry — Track exceptions and errors

Causality

CausalML — Estimate causal impacts from observational data
Pyro — Probabilistic modeling and inference

This mix of open source standards and dedicated ML tools provides powerful capabilities for end-to-end AI observability.

Conclusion

I’ve explored a comprehensive observability approach for machine learning systems including:

Structured logging with structlog across components
Metrics collection using OpenTelemetry and Prometheus
Distributed tracing to track requests end-to-end
Experiment tracking with MLflow
Dashboards for unified visibility
Anomaly detection for catching issues early
Retrospectives to fuel continuous improvement
Causality analysis to understand failures

Together these techniques provide holistic monitoring, debugging, and alerting for AI systems in production.

Robust observability unlocks the ability to continuously evolve complex, mission-critical machine learning applications with safety and confidence. It moves AI projects beyond prototyping to full production-scale impact.