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Model Performance Alerts: Detect and Monitor ML Model Degradation

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Imagine you deployed your AI model to production last Monday. 🎉
It was performing beautifully — 94% accuracy, fast responses, happy users.
Then on Friday, silently, something changed. The accuracy dropped to 61%.

Nobody noticed. No alarm went off. Users got wrong predictions for three whole days.
By the time someone spotted it, the damage was done — customer trust, gone.

This is called Model Performance Degradation — and it happens to every AI model in production.
The solution? Set up smart alerts that catch the drop the moment it happens. 🔔




🗺️ What Are We Going to Learn

  • 🤔 Why do AI models degrade in production? (The real reasons!)
  • 📊 What metrics should you monitor? (Not just accuracy!)
  • 🏗️ OCI Monitoring Service — the control tower for your AI model
  • 🔔 Setting up OCI Alarms — step-by-step with screenshots guide
  • 📬 Getting notified via Email, Slack, and PagerDuty
  • 🐍 Python code — custom metric emission to OCI Monitoring
  • 🤖 Advanced: Auto-healing with OCI Functions (self-fixing models!)
  • ✅ Do's and Don'ts

Why Do AI Models Degrade?

Your AI model was trained on data from the past.
But the real world keeps changing — and your model doesn't automatically know that.

💡 Simple Analogy — The Weather Forecaster:
Imagine you trained a weather forecaster only on summer data. ☀️
Then winter arrives. The forecaster keeps saying "sunny and warm" even when it's snowing! ❄️
The forecaster (your model) isn't wrong — it's just working with outdated knowledge.
This is called Data Drift — the most common cause of model degradation.

🔍 The 4 Main Reasons Models Degrade:

  • 📉 Data Drift — The real-world data your model receives changes over time.
    Example: A fraud detection model trained pre-COVID struggles with post-COVID spending patterns.
  • 🏷️ Concept Drift — The relationship between inputs and outputs changes.
    Example: Words that used to signal spam don't work anymore — spammers adapted!
  • 💾 Infrastructure Issues — Memory leaks, model version mismatches, corrupt preprocessing pipelines.
  • 📦 Data Quality Drop — Upstream data pipelines start sending missing values, wrong formats, or corrupted records.
Root Cause What Changes? Real Example
Data Drift Input data distribution shifts Customer age group changes after a product rebrand
Concept Drift Input→Output relationship changes Loan default patterns shift during economic recession
Infrastructure Issue Compute/memory/code errors New deployment overwrote preprocessing logic
Data Quality Drop Input data becomes noisy/broken A sensor starts sending NULL values instead of readings

📊 What Metrics Should You Monitor?

Most beginners think: "Just monitor accuracy!"
But in production, accuracy is only one piece of the puzzle.
Here are the metrics that truly matter:

🎯 Model Performance Metrics (The Brain):

  • Accuracy / F1 Score / Precision / Recall — Is the model giving correct predictions?
  • Prediction Confidence Score — Is the model becoming less certain about its answers?
  • Error Rate — How often is it flat-out wrong?

⚙️ Operational Metrics (The Body):

  • Latency (Response Time) — How long does each prediction take? (Target: under 200ms for APIs)
  • Throughput (Requests/Second) — How many predictions per second is it handling?
  • Memory & CPU Usage — Is the model eating too many resources?
  • Error HTTP Rate — Are API calls returning 500 errors suddenly?

📦 Data Health Metrics (The Food):

  • Input Feature Distribution — Are incoming values within the expected range?
  • Missing Value Rate — Is more than X% of input data now NULL or empty?
  • Data Volume Drop — Are you suddenly getting far fewer requests than normal?
🌟 The Golden Rule of Production AI Monitoring:
Monitor all three layers — model performance + operational health + data quality.
Fixing only one layer while ignoring the others is like treating a fever without finding its cause. 🤒

🏗️ The OCI Monitoring Architecture — Your Control Tower

Oracle Cloud Infrastructure has a powerful built-in service called OCI Monitoring.
It collects metrics, lets you set thresholds, and fires alerts when things go wrong.
Think of it as the control tower at an airport 🛫 — always watching, always ready to warn pilots.

🧱 The Key OCI Services Involved:

OCI Service Role in Monitoring Analogy
OCI Monitoring Collects and stores all metrics The hospital's vital signs monitor 🏥
OCI Alarms Triggers notifications when a metric crosses a threshold The emergency alarm bell 🔔
OCI Notifications Sends alerts to Email, Slack, PagerDuty, SMS The nurse who calls the doctor 📱
OCI Model Deployment Hosts your AI model as a REST API endpoint The patient in the hospital bed 🛏️
OCI Functions Auto-response actions (retrain, rollback, scale) The surgeon who fixes the problem 🩺

🔄 How It All Flows Together:

[ Your AI Model on OCI Model Deployment ]
    ⬇️ emits metrics every 60 seconds
[ OCI Monitoring — Metric Namespace ]
    ⬇️ checked against threshold (e.g., accuracy < 85%)
[ OCI Alarm — "ModelAccuracyDrop" ]
    ⬇️ alarm fires → sends event to OCI Notifications
[ OCI Notifications Topic ]
    ⬇️ fans out to multiple channels
[ 📧 Email ]   [ 💬 Slack ]   [ 📟 PagerDuty ]   [ 🔧 OCI Functions ]

🛠️ Step-by-Step Setup — Let's Build It!

🔑 Prerequisites (Do This First!):

  • ✅ An active OCI Account (free tier works fine for this tutorial)
  • ✅ An OCI Model Deployment endpoint already running (your AI model in production)
  • ✅ Python 3.9+ installed locally or in an OCI Data Science Notebook Session
  • ✅ OCI CLI configured (run oci setup config in your terminal)

📍 Step 1 — Create an OCI Notifications Topic

Before you can receive alerts, you need to create a Notification Topic.
Think of a Topic like a group chat 💬 — you add members (email, Slack, etc.)
and whenever an alarm fires, it sends a message to this group.

In the OCI Console:

  • Go to Developer Services → Application Integration → Notifications
  • Click "Create Topic"
  • Name it: model-performance-alerts
  • Click Create

Now add a subscription to this topic:

  • Click your new topic → Create Subscription
  • Protocol: Email → enter your email address
  • Click Create → check your inbox and confirm the subscription email ✅
✅ Pro Tip:
You can add multiple subscriptions to one topic — email, Slack webhook, PagerDuty, HTTPS endpoint.
All subscribers get notified when the alarm fires. One topic, many listeners! 📡

📍 Step 2 — Emit Custom Metrics from Your AI Model

OCI Monitoring automatically collects infrastructure metrics (CPU, RAM, HTTP errors).
But for model-specific metrics like accuracy or F1 Score,
you need to push them manually from your model's inference code. 📤

This is done using the OCI Monitoring PostMetricData API.
Think of it as your model filing a report card every few minutes to OCI.

📌 Code Block 1 — Install OCI SDK

📝 What will this code do?
This installs the official OCI Python SDK — the toolkit that lets your Python code
talk to OCI services like Monitoring, Alarms, and Notifications.
Think of it as downloading the walkie-talkie app so your model can radio the OCI control tower. 📻 
# Run this in your OCI Data Science Notebook terminal or local terminal
pip install oci --quiet

📌 Code Block 2 — Push Custom Model Metrics to OCI Monitoring

📝 What will this code do?
This is the core code that sends your model's performance numbers (accuracy, F1 score, latency)
up to OCI Monitoring — just like uploading your exam results to a school portal. 📊
Once these numbers are in OCI Monitoring, you can set alarms on them.
Replace the placeholder values (OCID, compartment ID) with your own from the OCI Console. 
import oci
import datetime

# -------------------------------------------------------
# CONFIGURATION — Replace these with your own OCI values
# -------------------------------------------------------
COMPARTMENT_ID = "ocid1.compartment.oc1..your-compartment-id"
NAMESPACE      = "your_company_ai_metrics"   # Custom namespace (you choose the name)
REGION         = "ap-mumbai-1"               # Your OCI region

# Initialise OCI Monitoring client using your local config (~/.oci/config)
config          = oci.config.from_file()
monitoring      = oci.monitoring.MonitoringClient(config)

# -------------------------------------------------------
# FUNCTION: Send a single metric value to OCI Monitoring
# -------------------------------------------------------
def emit_metric(metric_name, metric_value, model_name="my_fraud_model"):
    """
    Sends one metric reading to OCI Monitoring.
    metric_name  : e.g. "ModelAccuracy", "F1Score", "PredictionLatencyMs"
    metric_value : the actual number (e.g. 0.87 for 87% accuracy)
    model_name   : a label so you can filter metrics per model
    """

    # Build the metric data point
    metric_data = oci.monitoring.models.MetricDataDetails(
        namespace   = NAMESPACE,
        compartment_id = COMPARTMENT_ID,
        name        = metric_name,
        dimensions  = {"model_name": model_name,  # Tag: which model?
                       "environment": "production"},  # Tag: which env?
        datapoints  = [
            oci.monitoring.models.Datapoint(
                timestamp = datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%S.000Z"),
                value     = float(metric_value)
            )
        ]
    )

    # Send to OCI Monitoring
    post_metric_data_details = oci.monitoring.models.PostMetricDataDetails(
        metric_data = [metric_data]
    )

    response = monitoring.post_metric_data(
        post_metric_data_details = post_metric_data_details
    )

    print(f"  ✅ Emitted: {metric_name} = {metric_value}  (HTTP {response.status})")


# -------------------------------------------------------
# SIMULATE: After each batch of predictions, emit metrics
# -------------------------------------------------------
# In real code, these values come from your model evaluation
model_accuracy    = 0.87     # 87% accuracy this batch
model_f1_score    = 0.84     # F1 score
avg_latency_ms    = 142.5    # Average prediction latency in milliseconds
error_rate_pct    = 1.2      # 1.2% of predictions threw errors

print("📡 Emitting model metrics to OCI Monitoring...")
emit_metric("ModelAccuracy",         model_accuracy)
emit_metric("ModelF1Score",          model_f1_score)
emit_metric("PredictionLatencyMs",   avg_latency_ms)
emit_metric("PredictionErrorRate",   error_rate_pct)
print("\n🎉 All metrics emitted successfully!")

Output:

📡 Emitting model metrics to OCI Monitoring...
  ✅ Emitted: ModelAccuracy = 0.87  (HTTP 200)
  ✅ Emitted: ModelF1Score = 0.84  (HTTP 200)
  ✅ Emitted: PredictionLatencyMs = 142.5  (HTTP 200)
  ✅ Emitted: PredictionErrorRate = 1.2  (HTTP 200)

🎉 All metrics emitted successfully!

📌 Code Block 3 — Wrap Metric Emission in Your Inference Loop

📝 What will this code do?
In production, your model processes predictions in batches (e.g., every 5 minutes).
This code shows how to plug the metric emission right after every prediction batch —
so OCI Monitoring always has fresh, up-to-date numbers from your live model. 📈
It also measures actual latency using Python's time module — not a fake number! 
import time
from sklearn.metrics import accuracy_score, f1_score

def run_prediction_batch_and_report(model, X_batch, y_true_batch, model_name):
    """
    Runs one batch of predictions, evaluates quality,
    measures latency, and emits everything to OCI Monitoring.
    """

    # --- Step 1: Time the predictions ---
    start_time   = time.time()
    y_pred_batch = model.predict(X_batch)
    end_time     = time.time()

    # Calculate average latency in milliseconds
    avg_latency = ((end_time - start_time) / len(X_batch)) * 1000

    # --- Step 2: Calculate quality metrics ---
    accuracy = accuracy_score(y_true_batch, y_pred_batch)
    f1       = f1_score(y_true_batch, y_pred_batch, average='weighted')

    # --- Step 3: Calculate error rate ---
    errors    = sum(1 for yt, yp in zip(y_true_batch, y_pred_batch) if yt != yp)
    error_pct = (errors / len(y_true_batch)) * 100

    # --- Step 4: Emit all metrics to OCI ---
    emit_metric("ModelAccuracy",       accuracy,     model_name)
    emit_metric("ModelF1Score",        f1,           model_name)
    emit_metric("PredictionLatencyMs", avg_latency,  model_name)
    emit_metric("PredictionErrorRate", error_pct,    model_name)

    # --- Step 5: Print a summary ---
    print(f"\n📊 Batch Report — Model: {model_name}")
    print(f"   Accuracy    : {accuracy*100:.2f}%")
    print(f"   F1 Score    : {f1:.4f}")
    print(f"   Latency     : {avg_latency:.2f} ms/sample")
    print(f"   Error Rate  : {error_pct:.2f}%")

    return y_pred_batch

# Example usage (plug into your production inference loop):
# run_prediction_batch_and_report(my_model, X_test, y_test, "fraud_detector_v2")

📍 Step 3 — Create an OCI Alarm (The Actual Alert!)

Now that your model is sending metrics to OCI Monitoring,
it's time to create an Alarm — the rule that says: "If accuracy drops below X, fire!" 🔥

In the OCI Console:

  • Go to Observability & Management → Monitoring → Alarm Definitions
  • Click "Create Alarm"

Fill in these fields:

Field What to Enter Why?
Alarm Name ModelAccuracyDropAlert Descriptive name you'll recognise at 2am 😄
Metric Namespace your_company_ai_metrics Must match what you used in Code Block 2
Metric Name ModelAccuracy The specific metric to watch
Statistic mean() Average accuracy over the interval
Interval 5 minutes Check every 5 minutes
Operator + Threshold less than 0.85 Fire alarm if accuracy falls below 85%
Trigger Delay 3 minutes Wait 3 min before confirming — avoids false alarms
Notifications Topic model-performance-alerts The topic we created in Step 1

Click "Save alarm" — you're done! 🎉

💡 What does "Trigger Delay" mean?
Imagine your model's accuracy dips to 84% for just 30 seconds — maybe a brief data hiccup.
Without a delay, you'd get a 2am panic alert for nothing!
Setting a delay of 3–5 minutes means: "Only alarm if this lasts persistently". 😌

📍 Step 4 — Create Alarms via Python Code (Automation!)

Clicking through the console is fine for one alarm.
But if you have 10 models, you'll want to create alarms automatically using code.

📌 Code Block 4 — Create OCI Alarms Programmatically

📝 What will this code do?
This code creates three alarms at once using the OCI Python SDK:
one for accuracy drops, one for high latency, and one for high error rates.
Instead of clicking through the console three times, one Python run sets up everything.
Think of it as writing rules for a robot security guard —
you define what to watch for, and the robot watches 24/7 without breaks! 🤖 
import oci

# Load OCI config
config           = oci.config.from_file()
monitoring_mgmt  = oci.monitoring.MonitoringClient(config)

COMPARTMENT_ID   = "ocid1.compartment.oc1..your-compartment-id"
TOPIC_ID         = "ocid1.onstopic.oc1..your-topic-ocid"   # From Step 1
NAMESPACE        = "your_company_ai_metrics"
MODEL_NAME       = "fraud_detector_v2"

# -----------------------------------------------
# Define the alarms we want to create
# -----------------------------------------------
alarms_to_create = [
    {
        "name"        : "ModelAccuracyDropAlert",
        "metric"      : "ModelAccuracy",
        "operator"    : "lt",      # less than
        "threshold"   : 0.85,      # Alert if accuracy < 85%
        "severity"    : "CRITICAL",
        "message"     : "🚨 ALERT: Model accuracy has dropped below 85%! Immediate review required."
    },
    {
        "name"        : "HighLatencyAlert",
        "metric"      : "PredictionLatencyMs",
        "operator"    : "gt",      # greater than
        "threshold"   : 500.0,     # Alert if latency > 500ms
        "severity"    : "WARNING",
        "message"     : "⚠️ WARNING: Model prediction latency exceeded 500ms. Users may notice slowness."
    },
    {
        "name"        : "HighErrorRateAlert",
        "metric"      : "PredictionErrorRate",
        "operator"    : "gt",      # greater than
        "threshold"   : 5.0,       # Alert if error rate > 5%
        "severity"    : "CRITICAL",
        "message"     : "🚨 ALERT: Model error rate exceeded 5%! Something is seriously wrong."
    }
]

# -----------------------------------------------
# Create each alarm in OCI
# -----------------------------------------------
for alarm_config in alarms_to_create:

    # Build the MQL query (OCI Monitoring Query Language)
    mql_query = (
        f'{alarm_config["metric"]}[5m]{{model_name="{MODEL_NAME}"}}.mean()'
    )

    alarm_details = oci.monitoring.models.CreateAlarmDetails(
        display_name          = alarm_config["name"],
        compartment_id        = COMPARTMENT_ID,
        metric_compartment_id = COMPARTMENT_ID,
        namespace             = NAMESPACE,
        query                 = mql_query,
        severity              = alarm_config["severity"],
        destinations          = [TOPIC_ID],
        is_enabled            = True,
        body                  = alarm_config["message"],
        pending_duration      = "PT3M",   # 3-minute trigger delay (ISO 8601 format)
    )

    response = monitoring_mgmt.create_alarm(
        create_alarm_details = alarm_details
    )

    print(f"  ✅ Created: {alarm_config['name']}  (Severity: {alarm_config['severity']})")

print("\n🎉 All 3 alarms created and active in OCI Monitoring!")

Output:

  ✅ Created: ModelAccuracyDropAlert  (Severity: CRITICAL)
  ✅ Created: HighLatencyAlert  (Severity: WARNING)
  ✅ Created: HighErrorRateAlert  (Severity: CRITICAL)

🎉 All 3 alarms created and active in OCI Monitoring!

📍 Step 5 — Connect Slack for Real-Time Alerts 💬

Email alerts are great, but Slack notifications are seen instantly by your whole team.
Here's how to connect OCI Notifications to a Slack channel:

  • In Slack: Go to your channel → Integrations → Add an App → Incoming WebHooks.
    Copy the Webhook URL (looks like: https://hooks.slack.com/services/T.../B.../xxx)
  • In OCI Console → Notifications → your topic → Create Subscription:
    Protocol: HTTPS → paste your Slack Webhook URL → Create.

📌 Code Block 5 — Test Slack Alert Manually

📝 What will this code do?
This sends a test message directly to your Slack channel using Python.
It's like sending a test fire drill alert 🚒 before a real emergency.
This helps you confirm that your Slack webhook is correctly wired up
before you actually need it at 2am in a real incident! 
import urllib.request
import json

SLACK_WEBHOOK_URL = "https://hooks.slack.com/services/YOUR/WEBHOOK/URL"

def send_slack_alert(message, severity="WARNING"):
    """
    Sends a formatted message to a Slack channel via webhook.
    severity: "CRITICAL" shows red 🔴, "WARNING" shows yellow 🟡
    """

    emoji = "🔴" if severity == "CRITICAL" else "🟡"

    payload = {
        "text": f"{emoji} *OCI AI Model Alert* — {severity}",
        "attachments": [
            {
                "color"  : "#FF0000" if severity == "CRITICAL" else "#FFA500",
                "text"   : message,
                "footer" : "OCI AI Monitoring | Oracle Cloud",
                "ts"     : __import__('time').time()
            }
        ]
    }

    data = json.dumps(payload).encode('utf-8')
    req  = urllib.request.Request(
        SLACK_WEBHOOK_URL,
        data    = data,
        headers = {"Content-Type": "application/json"}
    )

    with urllib.request.urlopen(req) as response:
        print(f"  📤 Slack response: {response.status} {response.reason}")

# Test it!
send_slack_alert(
    message  = "🚨 Model accuracy has dropped to 72%! Last good reading was 91% 
at 14:30 UTC. Please investigate fraud_detector_v2.",
    severity = "CRITICAL"
)

Output (and what appears in Slack):

  📤 Slack response: 200 OK

--- In your Slack channel ---
🔴 OCI AI Model Alert — CRITICAL
🚨 Model accuracy has dropped to 72%! Last good reading was 91%
   at 14:30 UTC. Please investigate fraud_detector_v2.
OCI AI Monitoring | Oracle Cloud

🤖 Advanced: Auto-Healing with OCI Functions

Getting alerts is great. But what if your system could fix itself automatically? 🪄
With OCI Functions, when an alarm fires, it can trigger a serverless function
that automatically takes corrective action — no human needed!

💡 Think of it like a thermostat 🌡️:
When room temperature drops below 18°C → thermostat automatically turns on the heater.
You don't need to wake up and manually switch it on — it's self-healing.
OCI Functions can do the same for your AI model!

🔧 What Can Auto-Healing Actions Do?

  • Rollback to last stable model version — if accuracy drops, swap back to yesterday's good model.
  • Scale up compute — if latency spikes, automatically add more OCI instances behind the load balancer.
  • Trigger a retraining pipeline — kick off an OCI Data Science Job to retrain the model on fresh data.
  • Switch to a fallback model — serve a simpler but reliable backup model while the main one is fixed.

📌 Code Block 6 — OCI Function: Auto-Rollback on Accuracy Drop

📝 What will this code do?
This is the Python code you'd deploy inside an OCI Function.
When OCI Alarms trigger, they can call this function automatically.
The function checks which model version is currently deployed
and rolls it back to the last stable version — like pressing Ctrl+Z on your AI model! ↩️
You deploy this code to OCI Functions once, and it stands ready 24/7. 
import io
import json
import oci
import logging

# OCI Function entry point — this function runs when the alarm fires
def handler(ctx, data: io.BytesIO = None):
    """
    OCI Function triggered by OCI Alarm via OCI Notifications.
    Automatically rolls back to the last stable model version.
    """

    logging.getLogger().info("🚨 Auto-healing triggered by model performance alarm!")

    # Parse the incoming alarm event
    try:
        alarm_event = json.loads(data.getvalue())
        alarm_name  = alarm_event.get("title", "Unknown Alarm")
        alarm_type  = alarm_event.get("type", "FIRING")
        logging.getLogger().info(f"  Alarm: {alarm_name} | Type: {alarm_type}")
    except Exception as e:
        logging.getLogger().error(f"  Could not parse alarm event: {e}")
        return

    # Only act when alarm is FIRING (not when it resolves)
    if alarm_type != "FIRING":
        logging.getLogger().info("  Alarm resolved — no action needed. ✅")
        return

    # Initialise OCI Data Science client using instance principal auth
    # (OCI Functions use instance principal — no config file needed!)
    signer       = oci.auth.signers.get_resource_principals_signer()
    ds_client    = oci.data_science.DataScienceClient(config={}, signer=signer)

    # Your model deployment OCID (set this as a Function config variable)
    MODEL_DEPLOYMENT_OCID = "ocid1.datasciencemodeldeployment.oc1..your-deployment-id"
    STABLE_MODEL_OCID     = "ocid1.datasciencemodel.oc1..your-last-stable-model-id"

    try:
        # Update the deployment to use the last known stable model version
        update_details = oci.data_science.models.UpdateModelDeploymentDetails(
            model_deployment_configuration_details =
                oci.data_science.models.SingleModelDeploymentConfigurationDetails(
                    model_configuration_details =
                        oci.data_science.models.ModelConfigurationDetails(
                            model_id = STABLE_MODEL_OCID
                        )
                )
        )

        ds_client.update_model_deployment(
            model_deployment_id              = MODEL_DEPLOYMENT_OCID,
            update_model_deployment_details  = update_details
        )

        logging.getLogger().info("  ✅ Rollback successful! Stable model version restored.")
        return {"status": "rollback_complete", "model": STABLE_MODEL_OCID}

    except Exception as e:
        logging.getLogger().error(f"  ❌ Rollback failed: {e}")
        return {"status": "error", "message": str(e)}

📋 The Complete Alert Strategy — All Together

Here's a recommended set of alarms to set up for every production AI model on OCI:

Alarm Name Metric Threshold Severity Auto-Action
Accuracy Drop ModelAccuracy < 0.85 85% 🔴 CRITICAL Rollback model version
F1 Score Drop ModelF1Score < 0.80 0.80 🟡 WARNING Trigger retraining job
High Latency PredictionLatencyMs > 500 500ms 🟡 WARNING Scale up deployment
High Error Rate PredictionErrorRate > 5% 5% 🔴 CRITICAL Switch to fallback model
No Predictions RequestCount < 1 in 10 min 0 requests 🔴 CRITICAL Restart deployment endpoint
High CPU Usage CpuUtilization > 90% 90% 🟡 WARNING Scale out instances

✅ DO's and DON'Ts — Critical Rules!

✅ DO's:
  • Always emit metrics every 1–5 minutes — not once an hour. Hourly is too slow to catch problems!
  • Set different severity levels — WARNING for soft drops, CRITICAL for dangerous drops.
  • Always use a Trigger Delay (3–5 min) to avoid false alarms from temporary blips.
  • Monitor all three layers: model quality + operational health + data quality.
  • Test your alarms by intentionally sending bad metric values — like a fire drill. 🔥
  • Tag all metrics with dimensions (model_name, environment) so you can filter per model.
  • Keep a rollback version of every model ready — always have a Plan B in OCI Model Catalog.
❌ DON'Ts:
  • Don't monitor accuracy alone — a model can have high accuracy but terrible recall or latency.
  • Don't set thresholds too tight — e.g., alarm at 99.9% accuracy will fire constantly. Set realistic ones!
  • Don't skip testing your notification channel — what use is an alarm if nobody receives it?
  • Don't auto-rollback without logging why — always emit a log entry explaining what triggered the action.
  • Don't ignore WARNING-level alarms — they are early signals before things go truly wrong!
  • Don't forget to update thresholds when you retrain your model — a retrained model may have different baselines.

🔁 Quick Revision

  • 📉 Models degrade because the world changes but the model doesn't.
    This is called Data Drift and Concept Drift.
  • 📊 Monitor 3 layers: model quality metrics + operational metrics + data health.
  • 📡 OCI Monitoring = your control tower. Emit metrics from your model every few minutes.
  • 🔔 OCI Alarms = the alarm bell. Set rules like "accuracy < 85% → fire alert!"
  • 📬 OCI Notifications = the messenger. Sends alerts to Email, Slack, PagerDuty.
  • 🤖 OCI Functions = the auto-repair robot. Rollback, retrain, or scale — automatically!
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