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Adversarial ML Attack & Defend Recommenders in E-commerce
Published 9/2026
Created by Bayt Al Hikmah
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 110 Lectures ( 20h 47m ) | Size: 763.5 MB
From shilling attacks that fool your ranking model to a governed, sovereign recommender that survives them.
What you'll learn
Build a real recommender system from scratch - popularity baselines, implicit ALS matrix factorization and FAISS vector retrieval. Launch controlled adversarial attacks against your own recommender - random and bandwagon shilling, nuke attacks, session injection, low-and-slow poisoning. Detect manipulation using schema validation, bot-profile similarity features, Isolation Forest anomaly detection, time-window spike detection. Defend with evidence: quarantine pipelines, rank-recovery comparison Engineer the full MLOps stack - MLflow experiment tracking, DVC data versioning, Feast feature stores, Airflow/Kafka orchestration Serve and harden the model via FastAPI - health/readiness endpoints, fallback circuit breakers, canary releases, shadow-mode scoring, and load testing Deploy to Kubernetes with Helm, OPA policy-as-code, RBAC least privilege, Trivy container scanning, Syft SBOMs, and Cosign artifact signing. Govern privacy and compliance - pseudonymization, retention windows, DPIA-style checklists, user-deletion propagation, and a GDPR/EU AI Act/DORA-aware Architect for sovereignty - region-aware data residency configuration, federated evaluation patterns, and decentralized, reproducible research bundles. Deliver a capstone-grade sovereign, adversarially robust recommendation engine - attacked, defended, monitored, deployed, and defensible in an architectureRequirements
Knowledge: Basic Python (variables, functions, running scripts). No prior machine learning, recommender systems, or security experience required - Module 1 builds everything from a working baseline model up. Comfort with a terminal and copy-pasting commands. Basic SQL/pandas familiarity helps in the data-engineering modules but isn't required; every script is explained before you run it. Software (all free/open-source): Docker and Docker Compose, Git, Python 3.11+, uv (Python package manager, installed in Lab 1). Open-source ML/data stack used via uv add and containers: pandas, scikit-learn, PyTorch-adjacent libraries (implicit, FAISS), FastAPI, MLflow, DVC, Feast, DuckDB, PostgreSQL, MinIO, Kafka, Airflow. Security/MLOps tooling via containers: Trivy, Syft, Cosign, OPA, Locust - no license or account required. Optional for Modules 7-8: kind and kubectl for local Kubernetes labs - a lightweight local cluster, not a cloud account. Hardware: 20GB+ free disk space, 8GB+ RAM recommended (Kafka, PostgreSQL, MinIO, and Kubernetes running concurrently in later modules). No real e-commerce data, no real user data, and no live production system required - every lab uses a synthetic, seeded marketplace dataset built in Lab 3.Description
This course contains the use of artificial intelligence.
We only charge a fee solely for the time invested in building this comprehensive curriculum.
The "Our Bestsellers List Got Hacked" Problem
Recommendation engines are quiet, high-value infrastructure - they decide what customers see first, which products get momentum, and which sellers win the algorithm. That also makes them a target. A coordinated group of fake accounts can fabricate demand for a mediocre product, bury a competitor's listing, or slowly poison a model's understanding of what "popular" means - and by the time anyone notices, the damage is baked into weeks of training data. NIST's 2025 adversarial ML taxonomy, MITRE ATLAS, and the OWASP ML Top 10 all now treat this as a formal risk category. Most ML courses never mention it at all.
This course closes that gap directly: you build a real recommender system, then you attack it yourself - safely, on synthetic data, against your own lab infrastructure - and then you defend it with the same rigor a production ML security team would use. This is not a course about breaking real platforms. Every offensive technique here runs under a documented Red-Team Rules of Engagement, against synthetic data and owned local services only.
The Solution: 100 Labs, One Growing Adversarially-Tested System
Every lab follows the same pattern: a pre-flight check, a Git-based safety net, and a scripted rollback (make doctor, make snapshot, make rollback), so you build without fear of breaking your environment. By Lab 10 you'll have a synthetic marketplace dataset, a popularity-based recommender, and a live FastAPI endpoint serving real recommendations - a complete, working system in miniature. Everything after that adds real production depth.
What's Inside
Modules 1-2 build the actual recommender: a synthetic e-commerce dataset, offline evaluation metrics (precision/recall@k), a temporal train-test split that avoids leakage, an implicit ALS model, FAISS similar-item retrieval, candidate-generation/re-ranking separation, cold-start handling, and a documented model card and quality-gate before anything ships.Module 3 is the part almost no ML course covers: controlled adversarial attacks - random and bandwagon shilling, nuke attacks against a target item, sequential session injection, attack-budget modeling, low-and-slow poisoning that evades spike detectors, API-based model extraction probing, rate-pressure testing, membership-inference exploration, duplicate-event injection, and item-metadata poisoning - every one measured for actual rank lift and logged in an attack-results dashboard.Module 4 flips to defense: Pandera schema validation, duplicate detection, bot-profile similarity features, an Isolation Forest anomaly detector, time-window spike detection, slow-drift monitoring, evidence-preserving quarantine, and rank-recovery comparison that proves your defenses actually reversed the attack - plus data contracts, pseudonymization, retention windows, a DPIA-style checklist, and user-deletion propagation.Module 5 industrializes the ML lifecycle: DuckDB and PostgreSQL for data, MinIO object storage, MLflow experiment tracking, DVC data versioning, a Feast feature store with offline/online serving, Airflow and Kafka for batch and streaming orchestration, and CI pipelines with lint, test, and security checks.Module 6 hardens serving: containerized FastAPI with health/readiness endpoints, structured logging, Prometheus metrics, OpenTelemetry tracing, Grafana dashboards, fallback circuit breakers, canary releases, shadow-mode scoring, and Locust load testing against defined SLOs.Module 7 deploys and locks down the platform: Kubernetes via Kind, Helm packaging, namespace isolation, Kubernetes Secrets, OPA policy-as-code, Trivy image scanning, Syft SBOM generation, Cosign artifact signing, RBAC least privilege, and a compliance traceability matrix mapping every control to real evidence.Module 8 closes with sovereignty and reproducibility: region-aware data residency configuration, federated evaluation patterns that share metrics without moving user data, decentralized research bundles, reproducibility manifests, and a full Architecture Review Board packet.The Climax: Lab 100 - The Sovereign, Adversarially Robust Recommendation Engine
Lab 100 runs the entire system end-to-end: the pipeline executes, an attack is launched against your own recommender, a defense detects and quarantines it, rank recovery is measured, and the whole architecture - model, attacks, defenses, deployment, observability, security, compliance, and sovereignty - is packaged into a governance-ready capstone submission. This isn't a notebook you close and forget. It's a Git repository with working code, measurable attack impact, proven defense recovery, Kubernetes manifests, signed artifacts, and a review packet you could genuinely present to a senior architecture board.
Why Enroll Now
Adversarial ML is moving from academic paper to operational requirement faster than almost any other security discipline right now - NIST, MITRE ATLAS, and OWASP have all formalized it in the last two years, and job descriptions for ML engineer, MLOps, and AI security roles increasingly expect exactly this cross-functional skill set. Every tool in this course is free and open-source - PyTorch-adjacent libraries, MLflow, Feast, Kafka, OPA, Trivy, Cosign - so the system you build is entirely yours to keep, extend, and put directly into a portfolio that stands out from yet another Kaggle notebook.
Who this course is for
The ML Engineer Building Recommender or Ranking Systems You can train a model, but you've never had to think about what happens when a seller or bot farm decides to game your ranking. This course builds a real recommender - ALS, FAISS, candidate generation, re-ranking - then teaches you to attack and defend it, closing the exact gap between "the model works offline" and "the model survives production abuse." The Security Engineer Moving Into AI/ML Security You know threat modeling and red-teaming, but ML systems feel like a different animal - no CVEs, no familiar attack surface. This course maps NIST's AML taxonomy, MITRE ATLAS, and the OWASP ML Top 10 onto a real system you build yourself, with hands-on shilling attacks, model extraction probing, and membership-inference exploration - all against an owned lab target. The Senior MLOps/Platform Architect Owning Production ML Risk You're responsible for a recommender or personalization system in production, and "we'll add security later" isn't an acceptable answer to your leadership anymore. This course delivers the full stack you need to defend the architecture: MLflow, Kubernetes, OPA policy-as-code, SBOM/signing, GDPR-aligned governance, and a sovereign deployment topology - everything an architecture review board would ask for.Homepage
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