System-Design-Question

Add ML system design interview prep repository structure

Category: ml_system_design Date: 2026-03-11

System Design Discussion: Add ML System

Problem Statement: Design an Add ML system that allows users to add machine learning models, train them on a dataset, and serve predictions to users. The system should be scalable, fault-tolerant, and handle high traffic.

Requirements (Functional + Non-functional)

  1. Functional Requirements:
    • Add ML models to the system
    • Train ML models on a dataset
    • Serve predictions to users
    • Support multiple ML frameworks (e.g., TensorFlow, PyTorch)
    • Support multiple dataset formats (e.g., CSV, JSON)
  2. Non-functional Requirements:
    • Scale horizontally to handle high traffic
    • Ensure fault-tolerance in case of model or dataset failures
    • Optimize for low-latency predictions
    • Ensure data security and privacy

High-Level Architecture:

  1. Data Ingestion Layer:
    • Responsible for collecting and processing data from various sources (e.g., CSV, JSON)
    • Use Apache Kafka or Amazon Kinesis for high-throughput data ingestion
  2. Model Ingestion Layer:
    • Responsible for collecting and processing ML models from various sources (e.g., TensorFlow, PyTorch)
    • Use Docker containers to package and deploy models
  3. Model Training Layer:
    • Responsible for training ML models on the ingested dataset
    • Use Apache Spark or Hadoop for distributed training
  4. Model Serving Layer:
    • Responsible for serving predictions to users
    • Use TensorFlow Serving or AWS SageMaker for high-performance prediction serving
  5. API Gateway:
    • Responsible for handling user requests and routing them to the Model Serving Layer
    • Use AWS API Gateway or Google Cloud Endpoints for secure and scalable API management

Database Design:

  1. ML Model Store:
    • Use a NoSQL database (e.g., MongoDB, Cassandra) to store ML models and their metadata
  2. Dataset Store:
    • Use a relational database (e.g., MySQL, PostgreSQL) to store datasets and their metadata
  3. Prediction Store:
    • Use a time-series database (e.g., InfluxDB, OpenTSDB) to store prediction results and their metadata

Scaling Strategy:

  1. Horizontal Scaling:
    • Use load balancers (e.g., HAProxy, NGINX) to distribute traffic across multiple instances
    • Use autoscaling tools (e.g., AWS Auto Scaling, Google Cloud Auto Scaling) to dynamically adjust instance counts
  2. Vertical Scaling:
    • Use instance types with high CPU and memory capacity (e.g., AWS c5.xlarge, Google Cloud n1-standard-8)
  3. Sharding:
    • Use sharding techniques (e.g., horizontal partitioning, range-based partitioning) to distribute data across multiple instances

Bottlenecks:

  1. Data Ingestion:
    • High-throughput data ingestion can lead to data loss or corruption
  2. Model Training:
    • Training large ML models can consume significant computational resources and lead to long training times
  3. Model Serving:
    • High-performance prediction serving requires careful optimization and caching

Trade-offs:

  1. Scalability vs. Complexity:
    • Horizontal scaling can lead to increased complexity and costs
  2. Data Security vs. Data Sharing:
    • Ensuring data security and privacy can limit data sharing and collaboration

Add ML System Design Interview Prep Repository Structure:

Follow the first principle of system design: Separation of Concerns.

  1. data-ingestion: Contains code for data ingestion from various sources (e.g., CSV, JSON)
  2. model-ingestion: Contains code for model ingestion from various sources (e.g., TensorFlow, PyTorch)
  3. model-training: Contains code for distributed training of ML models using Apache Spark or Hadoop
  4. model-serving: Contains code for high-performance prediction serving using TensorFlow Serving or AWS SageMaker
  5. api-gateway: Contains code for secure and scalable API management using AWS API Gateway or Google Cloud Endpoints
  6. db-design: Contains database schema and design for ML model store, dataset store, and prediction store
  7. scaling-strategy: Contains code and documentation for horizontal scaling, vertical scaling, and sharding
  8. bottlenecks: Contains analysis and solutions for common bottlenecks in the system
  9. trade-offs: Contains analysis and trade-offs for key design decisions

Learning Resources:

  1. Apache Kafka: https://kafka.apache.org/
  2. TensorFlow Serving: https://www.tensorflow.org/tfx/serving
  3. AWS SageMaker: https://aws.amazon.com/sagemaker/
  4. Apache Spark: https://spark.apache.org/
  5. Google Cloud Endpoints: https://cloud.google.com/endpoints

Note: This is a high-level system design discussion, and the actual implementation may vary depending on the specific requirements and constraints of the project.

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