Manual ML Deployment vs Serverless ML

Developers should learn manual ML deployment when working on small projects, rapid prototyping, or in resource-constrained environments where setting up automated pipelines is overkill meets developers should use serverless ml for cost-effective, scalable ml applications where infrastructure management is a bottleneck, such as in startups or projects with variable workloads. Here's our take.

Manual ML Deployment

Nice Pick

Developers should learn manual ML deployment when working on small projects, rapid prototyping, or in resource-constrained environments where setting up automated pipelines is overkill

Pros

• It provides a foundational understanding of the deployment lifecycle, including model serialization, API creation, and infrastructure management, which is essential for troubleshooting and customizing deployments
• Related to: mlops, model-serving

Cons

• Specific tradeoffs depend on your use case

Serverless ML

Developers should use Serverless ML for cost-effective, scalable ML applications where infrastructure management is a bottleneck, such as in startups or projects with variable workloads

Pros

• It's ideal for real-time inference APIs, automated data pipelines, or proof-of-concept models that require rapid deployment without operational overhead
• Related to: aws-lambda, google-cloud-functions

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Manual ML Deployment is a methodology while Serverless ML is a platform. We picked Manual ML Deployment based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Manual ML Deployment wins

Based on overall popularity. Manual ML Deployment is more widely used, but Serverless ML excels in its own space.