v3.0.5 — Stability & Production Readiness Patch

🚀 Release Notes — v3.0.5

Stability & Production Readiness Patch

This release addresses all issues identified during an external code review. The focus was on reliability, configurability, observability, and CI stability.

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🔧 Bug Fixes & Critical Improvements

1. Consumer Reliability & Data Integrity

• Added group_id (orders_consumer_group) to prevent duplicate message reads on consumer restarts.
• Replaced blocking for message in consumer loop with a poll()-based loop that respects shutdown signals (SIGINT/SIGTERM). Containers now stop cleanly even when the topic is idle.
• Graceful shutdown is now fully reliable.

2. Environment Variables & Configuration

• CLICKHOUSE_HOST is now respected by all services:
  fastapi_app.py, streamlit_app.py, model_stub.py, and linear_regression.py. No more hard‑coded hostnames.
• API_URL is now configurable in streamlit_app.py (default: http://api:8000).

3. Kafka Dual‑Listener Setup

• Containers use kafka:9092.
• Host (CI, load tests, local runs) use localhost:29092.
• Both ports are exposed, enabling external tools to reach Kafka without DNS resolution issues.

4. API Startup & Lifespan

• Removed the 30‑second blocking sleep on startup.
• Migrated from deprecated @app.on_event to the modern lifespan context manager.
• Model training now starts in the background after a short initial delay without blocking the API.

5. Dependencies (Updated to Stable Versions)

Package	Old	New
kafka-python	2.0.2	2.2.20
numpy	1.23.5	1.26.4
scikit-learn	1.2.2	1.4.2
pandas	(unpinned)	2.2.0
docker (dev)	6.1.3	7.1.0

All versions are now pinned and tested against Python 3.10.

6. CI/CD Pipeline

• CI now builds images before starting services (docker compose up -d --build).
• Fixed CI command from docker-compose to docker compose for GitHub Actions compatibility.
• Re-added testcontainers to dev dependencies to unblock test imports (will be removed properly in v4.0.0).
• Removed fragile schema-drift test that caused false positives.

7. Grafana Dashboard

• Fixed pie chart to display all three channels (online_web, online_app, offline_store).
• Value options changed from Calculate to All values so nothing is hidden.
• Provisioning now uses a fixed UID (clickhouse), making dashboards portable across environments.

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📦 What’s Demo‑Only (Not a Bug)

Regression Model (Target Leakage)

The linear regression model currently uses amount as both a feature and the target.
This is intentional for the demo — it keeps the inference endpoint simple and predictable.
We will replace it with a proper feature‑based regression (using user stats, weekday, order count, etc.) in v4.0.0.

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🛠️ How to Upgrade

git pull origin main
docker compose down -v
docker compose up -d --build

Wait ~1 minute for services to initialise, then open:

Service	URL
API Docs	http://localhost:8000/docs
Dashboard	http://localhost:8501
Grafana	http://localhost:3001 (admin / admin)

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✅ What to Expect

• All services start without errors.
• /health returns model_ready: true.
• /predict/13?amount=200 returns a category and an amount.
• Grafana shows Total Revenue, A/B Test, Revenue by Channel (all three slices), Top Categories, Promo vs No Promo, and Weekend vs Weekday.
• All tests pass in CI and locally (pytest tests/ -v).

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🔮 Roadmap to v4.0.0

The next major release will focus on:

• Kubernetes (minikube) + Helm charts
• Dead Letter Queue (DLQ) for Kafka
• MLflow for experiment tracking and model registry
• Data drift monitoring with Evidently AI
• Proper regression model (no target leakage)

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🙏 Credits

Thanks to the external reviewer who provided the detailed bug report — it helped us harden the system significantly.

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📎 Links

• Repository: github.com/DevSpecOps/Pipeline-Prod-AIOps
• Latest release: v3.0.5

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Released on: 2026-07-02
Status: Stable · Production‑ready · Fully documented

v3.0.4 — Stable release with all bugfixes

🚀 Release Notes — v3.0.4

Stable release with all bugfixes applied

We are happy to announce the release of v3.0.4 — a fully stable and production‑ready version of Pipeline-Prod-AIOps.
This release closes all issues identified during external review and brings the project to a state ready for demonstration and deployment.

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✅ What's New & Fixed

🔧 Critical Fixes

• Kafka advertised listener — now uses internal kafka:9092 address, ensuring reliable connectivity between producer, consumer, and broker.
• Producer reliability — added flush() to guarantee messages are actually sent to Kafka (previously they were buffered and never delivered).
• Database column unification — renamed total_amount → amount across producer, consumer, API, and dashboard queries. No more mismatches.

📊 Grafana & Monitoring

• Datasource provisioning — fixed ClickHouse datasource with overwrite: true and explicit host field. No more manual tweaks after deployment.
• Dashboard portability — all panels now use fixed UID clickhouse, making the dashboard reusable across environments.
• Time filters removed — dashboard shows all available data immediately, no need to adjust time ranges.
• Pie chart fixed — revenue by channel now displays all three channels (online_web, online_app, offline_store).

🧪 Testing & CI

• Tests rewritten — replaced slow testcontainers with direct connection tests for speed and reliability.
• CI pipeline enhanced — now spins up full Docker Compose stack before running pytest, ensuring tests run against real services.
• All tests pass — API health, ClickHouse connection, Kafka producer connection — all green.

📦 Infrastructure

• Grafana provisioning — datasource and dashboard now fully configured via YAML, no manual setup required after docker-compose up.
• Producer data model — realistic sports retail data with balanced channels, loyalty cards, discounts, and A/B test groups.

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🛠️ How to Upgrade

git pull origin main
docker-compose down -v
docker-compose up -d --build

Wait ~1 minute for services to initialize, then open:

• API docs: http://localhost:8000/docs
• Streamlit dashboard: http://localhost:8501
• Grafana: http://localhost:3001 (admin / admin)

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📊 What You'll See

• Data flowing — real‑time order generation into ClickHouse
• Grafana dashboard — revenue, A/B test results, channel breakdown, top categories, promo analysis
• Prometheus metrics — API health, cache hits, predictions count
• CI/CD pipeline — automated tests on every push

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🎯 What's Next (Roadmap)

We are already planning the next major release — v4.0.0 — which will include:

• Kubernetes (minikube) deployment with Helm charts
• Dead Letter Queue (DLQ) for Kafka
• MLflow for experiment tracking and model registry
• Data drift monitoring with Evidently AI

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🙏 Special Thanks

To everyone who contributed feedback, tested the system, and helped make this release stable.

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📎 Links

• Repository: github.com/DevSpecOps/Pipeline-Prod-AIOps
• Latest release: v3.0.4

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Released on: 2026-06-29
Stable · Production‑ready · Fully documented

v3.0.1 — Load Tests & Kafka Fixes

Fixes

• Kafka advertised listener — Kafka is now accessible both from Docker network and from host (localhost).
• Load tests — fixed DNS errors and interrupt handling (Ctrl+C).
• Consumer lag test — now correctly finishes and saves the plot.
• .gitignore — added *.html and *.log for test results.

How to run load tests

python load_tests/test_api_load.py
python load_tests/test_clickhouse_fill.py
python load_tests/test_consumer_lag.py

v3.0.0 — Sports Retail MLOps Pipeline

What's Included

• Realistic data generation for sports retail (categories, brands, prices, loyalty cards, discounts, A/B testing)
• ML models (classification + regression) with automatic retraining
• FastAPI with caching, Prometheus metrics, health checks
• Streamlit dashboard for real-time monitoring
• Grafana with business dashboard (revenue, channels, A/B tests, categories, promo)
• Full containerization (Docker Compose)
• Kubernetes manifests (ready for deployment)
• Load testing suite (Locust, ClickHouse fill, consumer lag)
• CI/CD (GitHub Actions)
• Comprehensive documentation (README)

How to Run

git clone https://github.com/DevSpecOps/Pipeline-Prod-AIOps.git
cd Pipeline-Prod-AIOps
docker-compose up -d --build

For details, see README.md.

v2.0.0 — Production-ready MLOps pipeline

• Kubernetes manifests (k8s/)
• Realistic data generation (producer.py with lognormal distribution)
• Auto-retraining with 20s interval and 30s startup delay
• In-memory cache with TTL (configurable)
• Prometheus metrics: predictions_total, cache_hits_total
• Grafana provisioning (dashboard auto-loads from repo)
• Persistent Grafana volume (dashboards survive restarts)
• Health check endpoint (/health)
• Load testing scripts (test_all.ps1 / test_all.sh)
• Updated README with Kubernetes and monitoring sections"

v1.0.0 — Production-ready MLOps pipeline

What's Included

• Full MLOps pipeline: Kafka → ClickHouse → FastAPI → Streamlit
• Async API with caching
• ML models (classification + regression)
• Monitoring stack (Prometheus + Grafana)
• CI/CD with GitHub Actions
• Dockerized services with healthchecks