How to decide on Model Deployment Using Sagemaker in AWS

Anurag Singh
4 min readJun 7, 2023

AWS Sagemaker is a managed instance to Build, Train, Tune and Deploy Data Science models. Below section describes the components within SageMaker for end to end ML cycle

Prepare

SageMaker Ground Truth- Label Training Data for ML

SageMaker Data Wrangler- Aggregate and Prepare Data for ML

SageMaker Processing- Built in Python, Bring Your Own R/Spark

SageMaker Feature Store- Store, Update, retrieve and share features.

SageMaker Clarify- DetectBias and understand model predictions

Build

SageMaker Studio Notebooks- Jupyter Notebooks with elastic compute and sharing

Built-in and Bring your-own Algorithms- Dozens of Optimized Algorithms inbuilt or bring your own

Local Mode- Test and Prototype on my Local Machine

SageMaker Autopilot- Automatically create ML models with full visibility

SageMaker Jumpstart- Prebuilt solutions for common use cases

Train and Tune

Managed Trainings-Distributed Infrastructure Management

SageMaker Experiments-Capture Organize and Compare every step

Automatic Model Tuning-Hyperparameter optimization

Distributed Training-Training for Large Datasets and Model

SageMaker Debugger- Debug and Profile Training runs

Managed Spot Training- Reduce Training Cost by 90%

Deploy and Manage

Managed Deployment- Fully Managed Ultra Low Latency, high throughput

Kubernetes and Kubeflow Integration- Simplify Kubernetes based ML

Multi-Model Endpoints- Reduce cost by hosting multiple models per instance.

SageMaker Model Monitor- Maintain accuracy of deployed models

SageMaker Edge Manager-Manage and Monitore models on edge devices

SageMaker Pipelines- Workflow Orchestration and Automation

Type of Inference Patterns and How to Make a Choice.

The below flowchart helps you decide on which type of Inference is best suited for your workload

Real-Time Inference:

Real-time inference is ideal for online inferences that have low latency or high throughput requirements. Use real-time inference for a persistent and fully managed endpoint (REST API) that can handle sustained traffic, backed by the instance type of your choice. Real-time inference can support payload sizes up to 6 MB and processing times of 60 seconds.

Host a Single Model for Real-Time Inference when you need to expose your trained model to the outside world( refer flowchart above)

Multi-model Endpoint for Real-Time Inference In some use cases we may have large number of similar models without a need to access all the models at same time. We can host multiple models in one container with direct invocation to target model which happens dynamically from S# under the hood. This helps in improving resource utilization and cost savings.

Multi-container Endpoint for Real-Time Inference In some use cases we may have multiple algorithms all having different ML frameworks. We can host these multiple models in multiple distinct containers( up to 15 containers) and model endpoint can be invoked directly or serially based on events. It avoids the cold start problem associated with Multi-model Endpoint.Deploy any combination of pretrained SageMaker built-in algorithms and custom algorithms packaged in Docker containers. We can Combine pre-processing, predictions, and post-processing data science tasks using this approach.

Asynchronous Inference:

Asynchronous inference is ideal when you want to queue requests and have large payloads with long processing times. Asynchronous Inference can support payloads up to 1 GB and long processing times up to one hour. You can also scale down your endpoint to 0 when there are no requests to process. Ideal for large payload up to 1GB,Longer processing timeout up to 15 min, Autoscaling (down to 0 instance),Suitable for CV/NLP use cases.

Batch Transform

Batch transform is suitable for offline processing when large amounts of data are available upfront and you don’t need a persistent endpoint. You can also use batch transform for pre-processing datasets. It can support payload sizes of GBs for large datasets and processing times of days. Suitable for use cases if I need to serve predictions on a schedule and pay only for what we use.

Serverless Inference

I do not want to deployment !manage the underlying infrastructure when deploying models. I want the model to automatically scale go for Serverless Inference

Serverless Inference is ideal when you have intermittent or unpredictable traffic patterns. SageMaker Automatically spins up and manages compute resources it manages all of the underlying infrastructure, so there’s no need to manage instances or scaling policies. It comes with managed logging and monitoring the best is we pay only for what you use and not for idle time. It can support payload sizes up to 4 MB and processing times up to 60 seconds. Pay only for what you use, billed in milliseconds. No need to set scaling policies.

Go ahead and make a choice in terms of selecting the right approach for your use cases.

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Anurag Singh

A visionary Gen AI, Data Science, Machine Learning, MLOPS and Big Data Leader/ Architect