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On-Premise MLOps Platform

This document outlines the architecture and components of an on-premise MLOps platform for a cell detection system. The platform is designed to enable multiple users to train, tune, and deploy machine learning (ML) models securely and efficiently using a shared dataset. The architecture supports secure isolation for users, incorporates guardrails for compliance and right resource usage, and leverages state-of-the-art tools and technologies to ensure scalability and performance.

High-Level Requirements​

  1. User Isolation: Each user operates in an isolated environment.
  2. Dataset Sharing: A central repository for a shared dataset of 1000-2000 human cell images.
  3. Scalability: Horizontally scalable infrastructure to accommodate up to 8-10 users per system.
  4. GPU Support: CUDA-enabled GPUs for efficient training and hyperparameter tuning.
  5. Security: VPN access, role-based access controls (RBAC), and data encryption.
  6. MLOps Features:
    • Experiment tracking
    • Model management and repository
    • Automated hyperparameter tuning
    • Workflow orchestration
  7. On-Premise Deployment: Ensures compliance with local data privacy and security requirements.

Architecture Diagram​

The architecture consists of the following layers:

  1. User Interface Layer
  2. Compute and Orchestration Layer
  3. Data Management Layer
  4. Model Deployment and Serving Layer

MLOps Platform

https://drive.google.com/file/d/1k1ZNZ_1UO-cFdyQ9IVlCPhFpYzJDL3gF/view?usp=drive_link

Components Overview​

  • User Workstations: Access via secure VPN.
  • Kubernetes Cluster: Manages isolated namespaces and workloads.
  • JupyterLab/Notebooks: Interactive environments for experimentation.
  • Kubeflow Pipelines: Orchestrates workflows.
  • MLflow: Tracks experiments and manages models.
  • GPU Cluster: Enables high-performance training.
  • Dataset Storage: Centralized repository for images.
  • Model Repository: Stores trained models for deployment.
  • Terraform + Jenkins: Automates infrastructure setup.

Component Details​

1. User Workstations​

  • Purpose: Provide data scientists with secure access to the platform.
  • Key Features:
    • Access via secure VPN.
    • Interface with JupyterLab for experimentation.
    • RBAC ensures controlled access.

2. Kubernetes Cluster​

  • Purpose: Core orchestration layer for resource allocation and isolation.
  • Technologies:
    • Kubernetes for container orchestration.
    • Namespace isolation for user environments.
  • Key Features:
    • Auto-scaling to handle increased workloads.
    • Secure communication within the cluster.

3. JupyterLab/Notebooks​

  • Purpose: Provide an interactive workspace for data scientists.
  • Technologies: Dockerized JupyterLab instances.
  • Key Features:
    • Pre-installed libraries (TensorFlow, Keras, etc.).
    • Isolated environments for each user.

4. Dataset Storage​

  • Purpose: Centralized storage for shared datasets.
  • Technologies: Network-attached storage (NAS) with encryption.
  • Key Features:
    • Access control for user security.
    • High availability and redundancy.

5. GPU Cluster​

  • Purpose: Accelerate model training and hyperparameter tuning.
  • Technologies: CUDA-enabled GPUs.
  • Key Features:
    • Resource allocation based on user workloads.
    • Integrated with Kubernetes for scaling.

6. Kubeflow Pipelines​

  • Purpose: Orchestrate ML workflows.
  • Key Features:
    • Automates data preprocessing, training, and evaluation.
    • Logs and monitors pipeline runs.

7. MLflow​

  • Purpose: Manage experiments, track metrics, and store models.
  • Key Features:
    • Experiment tracking with metrics visualization.
    • Model versioning for reproducibility.

8. Model Repository​

  • Purpose: Store and manage trained models.
  • Technologies: S3-compatible object storage or MinIO.
  • Key Features:
    • Support for TensorFlow and Keras models.
    • Integration with model deployment tools.

9. Terraform + Jenkins​

  • Purpose: Automate the deployment and management of infrastructure.
  • Key Features:
    • Terraform for infrastructure as code (IaC).
    • Jenkins for CI/CD pipelines.

10. Model Serving/Inference​

  • Purpose: Deploy models for production use.
  • Technologies: TensorFlow Serving, FastAPI.
  • Key Features:
    • RESTful API for real-time inference.
    • Scalable deployment using Kubernetes.

Security Measures​

  • VPN Access: Ensures secure access to the platform.
  • RBAC: Role-based access controls for fine-grained permissions.
  • Data Encryption: Encrypt sensitive datasets at rest and in transit.
  • Namespace Isolation: Isolate user environments in Kubernetes.

Workflow​

  1. Data Preparation:
    • Users upload data to the shared storage.
    • Preprocessing pipelines prepare data for training.
  2. Model Training:
    • Users launch training jobs using JupyterLab.
    • Kubernetes schedules GPU resources for training.
  3. Hyperparameter Tuning:
    • Leverage Kubeflow Pipelines for automated tuning.
  4. Experiment Tracking:
    • Users log metrics and parameters in MLflow.
  5. Model Deployment:
    • Trained models are stored in the repository.
    • Deployed using TensorFlow Serving or FastAPI.

Scalability and Future Enhancements​

  • Horizontal Scaling: Add more nodes to the Kubernetes cluster.
  • Additional Tools: Integrate advanced tools like SageMaker for hybrid workloads.
  • Federated Learning: Enable collaboration across multiple locations.

Conclusion​

This architecture provides a robust, secure, and scalable on-premise MLOps platform tailored for cell detection systems. The integration of modern tools ensures efficiency while maintaining high security and user isolation.