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MLOps Automation for Training and Production Monitoring of Network Anomaly Detection Applications

Description

This project provides an automated MLOps platform for a Network Anomaly Detection Application. The platform manages the complete machine learning lifecycle, including dataset preparation, model training, experiment tracking, model versioning, deployment, and production monitoring. It automatically retrains models when required and monitors model performance and prediction behavior after deployment.

Aim

To build a scalable MLOps platform using Kubeflow, MLflow, KServe, Prometheus, and Grafana to automate model training, deployment, and production monitoring for Network Anomaly Detection Applications.

Objectives

01 Automate the machine learning model training workflow.
02 Track experiments, model versions, and training results.
03 Automate deployment of approved anomaly detection models.
04 Monitor model and application performance in production.
05 Detect model degradation and abnormal prediction behavior.
06 Provide a standardized and repeatable MLOps lifecycle.

Application Workflow

01

Stage 1. User Login & Authentication

Process

The network administrator logs into the application using registered credentials.

Tools
Keycloak Kubernetes
Implementation

The application sends the login request to Keycloak. Keycloak validates the user's identity and provides access to the application.

02

Stage 2. Network Monitoring

Process

The administrator views the current network status and connected network resources.

Tools
Kubernetes PostgreSQL
Implementation

The application retrieves network information and displays connected devices, network status, traffic information, and system details.

03

Stage 3. Network Data Collection

Process

The application collects network traffic and operational information from monitored network resources.

Tools
Python PostgreSQL Kubernetes
Implementation

The application receives network information such as traffic volume, connection details, packet statistics, and network events and stores the required records.

04

Stage 4. Network Activity Analysis

Process

The administrator reviews network activity and identifies unusual network events based on configured monitoring rules.

Tools
Python PostgreSQL
Implementation

The application processes the collected network information and compares it with predefined thresholds or rules to identify unusual activity.

05

Stage 5. Event & Alert Management

Process

The application records and displays network events that require administrator attention.

Tools
Python PostgreSQL Kubernetes
Implementation

The application creates event records, assigns appropriate status or severity, and displays alerts to the administrator.

06

Stage 6. Network Reports

Process

The administrator generates reports about network activity and events.

Tools
PostgreSQL
Implementation

The application retrieves network records from PostgreSQL, processes the required information, and generates the requested reports.

07

Stage 7. Network Status & History

Process

The administrator reviews historical network activity and previously recorded events.

Tools
PostgreSQL
Implementation

The application retrieves historical records from PostgreSQL and presents network activity, event history, and status information.

08

Stage 8. Logout

Process

The administrator completes the monitoring activity and logs out.

Tools
Keycloak Kubernetes
Implementation

The application terminates the user's session through Keycloak.

Cloud Infrastructure and Tools

MLOps Workflow Orchestration Platform Kubeflow

Manages machine learning workflows including data preparation, model training, validation, and retraining.

ML Experiment Tracking & Model Registry MLflow

Tracks experiments, parameters, metrics, model versions, and approved models throughout the ML lifecycle.

Model Serving Platform KServe

Deploys trained anomaly detection models as scalable inference services on Kubernetes.

Machine Learning Framework Scikit-learn

Train the anomaly detection model and perform data preparation and prediction processing.

Container Orchestration Platform Kubernetes

Runs and manages the MLOps platform, training workloads, model services, and application containers.

Container Packaging Platform Docker

Packages the ML application, training components, and model-serving services into containers.

Metrics Collection Platform Prometheus

Collects infrastructure, application, and model-serving metrics for production monitoring.

Monitoring & Visualization Platform Grafana

Provides dashboards for monitoring model-serving performance, Kubernetes resources, and application metrics.

Database PostgreSQL

Stores network data, anomaly detection results, application information, and related records.

Target Compute Infrastructure Cloud EC2 Instances

Provides the virtual servers used to run the self-hosted Kubernetes cluster and MLOps workloads.

Server Operating System Substrate Ubuntu Server 24.04 LTS

Provides the Linux operating system for the EC2 servers hosting the Kubernetes and MLOps environment.

Cloud Networking Plane Cloud VPC

Provides the secure network for the MLOps infrastructure and separates internal services from external access.

Security Controls & Firewalls Cloud Security Groups + Network ACLs

Control network traffic and protect the MLOps infrastructure from unauthorized access.

Cloud Storage Interface Cloud S3

Stores training datasets, processed data, model artifacts, and archived MLOps data.

Implementation Process

01
Step 1 – Analyze the Application and Define MLOps Requirements
  • Review the Network Anomaly Detection Application architecture and data flow.
  • Identify the network data and features required for model training.
  • Define model performance metrics and anomaly detection requirements.
  • Define model versioning, deployment, and retraining requirements.
  • Identify the required CPU, memory, storage, and network resources.
02
Step 2 – Create the Cloud and Kubernetes Environment
  • Create an cloud VPC with the required public and private subnets.
  • Create cloud EC2 servers using Ubuntu Server 24.04 LTS.
  • Configure the self-hosted Kubernetes cluster.
  • Configure namespaces, networking, storage, and resource limits.
  • Configure cloud S3 for datasets and model artifacts.
03
Step 3 – Configure the MLOps Training Platform
  • Deploy Kubeflow on Kubernetes.
  • Deploy MLflow for experiment tracking and model management.
  • Create the model training pipeline.
  • Configure training, validation, and model evaluation stages.
  • Store approved model artifacts in the model registry and S3.
04
Step 4 – Deploy and Monitor the Model
  • Deploy the approved model using KServe.
  • Configure the model inference service on Kubernetes.
  • Configure Prometheus to collect application and model-serving metrics.
  • Create Grafana dashboards for production monitoring.
  • Configure model and application performance monitoring.
05
Step 5 – Test and Automate the MLOps Lifecycle
  • Run training using a sample network dataset.
  • Verify experiment tracking and model versioning in MLflow.
  • Test model deployment and anomaly predictions.
  • Introduce new network data and verify production monitoring.
  • Configure retraining and redeployment when model performance requires improvement.

Proposed Solution

The proposed solution provides an automated MLOps platform using Kubeflow, MLflow, KServe, Prometheus, and Grafana. Kubeflow manages model training workflows, MLflow tracks experiments and model versions, and KServe deploys approved models on Kubernetes. Prometheus and Grafana monitor the production environment, while Cloud EC2, VPC, S3, and EBS provide the required cloud infrastructure and storage.

Benefits

Automated Model Training: Kubeflow manages repeatable training workflows.
Model Version Tracking: MLflow records experiments and model versions.
Automated Deployment: KServe deploys approved models on Kubernetes.
Production Monitoring: Prometheus and Grafana provide continuous visibility.
Faster Retraining: New models can be trained and deployed systematically.
Standardized MLOps: The same lifecycle can be applied across environments.

Challenges

Model Performance: Detection accuracy may change as network behavior changes.
Data Quality: Poor or incomplete network data can affect model results.
Model Drift: Changing traffic patterns can reduce model effectiveness.
Resource Usage: Training and inference workloads require sufficient compute resources.
Monitoring Complexity: Production ML systems generate multiple application and model metrics.
Retraining Management: Retraining must be controlled to avoid deploying poorly performing models.
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