Production Ml Deployment Guide

Workflow

1. 1

   Define Deployment Requirements

   2-3 days

   Specify latency targets, throughput needs, and deployment pattern (batch, API, edge).

   Inference LatencyTOML to JSON

   Show tips (2)
2. 2

   Size Infrastructure

   1-2 days

   Calculate inference latency, model compression needs, and pipeline throughput.

   Inference LatencyModel Compression Ratio

   Show tips (2)
3. 3

   Build and Deploy Pipeline

   1-2 weeks

   Containerize model, set up CI/CD, deploy to staging, and configure monitoring.

   Inference LatencyModel Compression RatioData Pipeline Throughput

   Show tips (2)
4. 4

   Validate and Monitor

   3-5 days

   Run load tests, verify prediction quality in production, and enable drift monitoring.

   Model Compression RatioGPU Memory EstimatorPercentage Calculator

   Show tips (2)

Tools Used

• Inference Latency
  Primary

  Estimate serving latency to verify it meets SLA requirements.

• Model Compression Ratio
  Primary

  Evaluate compression options to meet latency or size constraints.

• Data Pipeline Throughput
  Supporting

  Size data pipeline capacity for batch or streaming inference.

• GPU Memory Estimator
  Reference

  Verify GPU memory for serving infrastructure sizing.

Reference Materials

• Shadow First

  Never replace a production model without running the new version in shadow mode first.

• Feature Store

  Centralize feature computation — training-serving skew is the top cause of production ML failures.

• Serverless Inference

  Serverless endpoints scale to zero — cost-effective for intermittent prediction workloads.

Related Scenarios

• Ml Model Training Pipeline Guide
  Data ScienceIntermediate4 tools
• Deep Learning Gpu Setup
  Data ScienceIntermediate4 tools
• Ml Project Timeline Guide
  Data ScienceIntermediate4 tools