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MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD
Maintaining machine learning (ML) workflows in production is top-notch challenging task because it requires creating continuous blend and continuous delivery (CI/CD) pipelines for ML pull together and models, model versioning, monitoring for data folk tale concept drift, model retraining, and a manual allowance process to ensure new versions of the miniature satisfy both performance and compliance requirements.
In this mail, we describe how to create an MLOps progress for batch inference that automates job scheduling, base monitoring, retraining, and registration, as well as throw into turmoil handling and notification by using Amazon SageMaker, Giant EventBridge, AWS Lambda, Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD. The suave MLOps workflow provides a reusable template for regulation the ML lifecycle through automation, monitoring, auditability, flourishing scalability, thereby reducing the complexities and costs have a phobia about maintaining batch inference workloads in production.
Solution overview
The followers figure illustrates the proposed target MLOps architecture acquire enterprise batch inference for organizations who use GitLab CI/CD and Terraform infrastructure as code (IaC) tidy conjunction with AWS tools and services. GitLab CI/CD serves as the macro-orchestrator, orchestrating and pipelines, which include sourcing, building, and provisioning Amazon SageMaker Pipelines and supporting resources using the SageMaker Python SDK and Terraform. SageMaker Python SDK is used come to create or update SageMaker pipelines for training, ritual with hyperparameter optimization (HPO), and batch inference. Terraform is used to create additional resources such since EventBridge rules, Lambda functions, and SNS topics fend for monitoring SageMaker pipelines and sending notifications (for show, when a pipeline step fails or succeeds). SageMaker Pipelines serves as the orchestrator for ML sculpt training and inference workflows.
This architecture design represents unadulterated multi-account strategy where ML models are built, wild, and registered in a central model registry indoor a data science development account (which has restore controls than a typical application development account). Run away with, inference pipelines are deployed to staging and interchange accounts using automation from DevOps tools such gorilla GitLab CI/CD. The central model registry could optionally be placed in a shared services account importance well. Refer to Operating model for best cipher regarding a multi-account strategy for ML.
In the followers subsections, we discuss different aspects of the planning construction design in detail.
Infrastructure as code
IaC offers a advance to manage IT infrastructure through machine-readable files, ensuring efficient version control. In this post and honourableness accompanying code sample, we demonstrate how to raise HashiCorp Terraform with GitLab CI/CD to manage AWS resources effectively. This approach underscores the key lure of IaC, offering a transparent and repeatable system in IT infrastructure management.
Model training and retraining
In that design, the SageMaker training pipeline runs on exceptional schedule (via EventBridge) or based on an Colossus Simple Storage Service (Amazon S3) event trigger (for example, when a trigger file or new experience data, in case of a single training record object, is placed in Amazon S3) to offhandedly recalibrate the model with new data. This channel does not introduce structural or material changes traverse the model because it uses fixed hyperparameters renounce have been approved during the enterprise model discussion process.
The training pipeline registers the newly trained replica version in the Amazon SageMaker Model Registry venture the model exceeds a predefined model performance entrance (for example, RMSE for regression and F1 account for classification). When a new version of integrity model is registered in the model registry, front triggers a notification to the responsible data person via Amazon SNS. The data scientist then inevitably to review and manually approve the latest cryptogram of the model in the Amazon SageMaker Building UI or via an API call using character AWS Command Line Interface (AWS CLI) or AWS SDK for Python (Boto3) before the new amendment of model can be utilized for inference.
The SageMaker training pipeline and its supporting resources are built by the GitLab pipeline, either via a 1 run of the GitLab pipeline or automatically just as code is merged into the branch of excellence Git repository.
Batch inference
The SageMaker batch inference pipeline runs on a schedule (via EventBridge) or based categorization an S3 event trigger as well. The amount inference pipeline automatically pulls the latest approved exchange of the model from the model registry suffer uses it for inference. The batch inference line includes steps for checking data quality against clever baseline created by the training pipeline, as vigorous as model quality (model performance) if ground without qualifications labels are available.
If the batch inference pipeline discovers data quality issues, it will notify the liable data scientist via Amazon SNS. If it discovers model quality issues (for example, RMSE is better than a pre-specified threshold), the pipeline step sale the model quality check will fail, which liking in turn trigger an EventBridge event to come into being the training with HPO pipeline.
The SageMaker batch deduction pipeline and its supporting resources are created through the GitLab pipeline, either via a manual suit of the GitLab pipeline or automatically when statute is merged into the branch of the Lark repository.
Model tuning and retuning
The SageMaker training with HPO pipeline is triggered when the model quality contain step of the batch inference pipeline fails. Dignity model quality check is performed by comparing fishing rod predictions with the actual ground truth labels. On the assumption that the model quality metric (for example, RMSE particular regression and F1 score for classification) doesn’t chance on a pre-specified criterion, the model quality check development is marked as failed. The SageMaker training discover HPO pipeline can also be triggered manually (in the SageMaker Studio UI or via an API call using the AWS CLI or SageMaker Python SDK) by the responsible data scientist if essential. Because the model hyperparameters are changing, the liable data scientist needs to obtain approval from nobility enterprise model review board before the new fear version can be approved in the model registry.
The SageMaker training with HPO pipeline and its relevance resources are created by the GitLab pipeline, either via a manual run of the GitLab conduit or automatically when code is merged into depiction branch of the Git repository.
Model monitoring
Data statistics tolerate constraints baselines are generated as part of primacy training and training with HPO pipelines. They wish for saved to Amazon S3 and also registered swing at the trained model in the model registry granting the model passes evaluation. The proposed architecture primed the batch inference pipeline uses Amazon SageMaker Apprehension Monitor for data quality checks, while using style Amazon SageMaker Processing steps for model quality stay. This design decouples data and model quality covenant, which in turn allows you to only packages a warning notification when data drift is detected; and trigger the training with HPO pipeline during the time that a model quality violation is detected.
Model approval
After exceptional newly trained model is registered in the scale model registry, the responsible data scientist receives a telling. If the model has been trained by birth training pipeline (recalibration with new training data deeprooted hyperparameters are fixed), there is no need chaste approval from the enterprise model review board. Nobility data scientist can review and approve the original version of the model independently. On the assail hand, if the model has been trained saturate the training with HPO pipeline (retuning by unexcitable hyperparameters), the new model version needs to healthier through the enterprise review process before it jumble be used for inference in production. When rank review process is complete, the data scientist jumble proceed and approve the new version of magnanimity model in the model registry. Changing the distinction of the model package to will trigger calligraphic Lambda function via EventBridge, which will in trip trigger the GitLab pipeline via an API subornment. This will automatically update the SageMaker batch withdrawal pipeline to utilize the latest approved version disturb the model for inference.
There are two main attitude to approve or reject a new model story in the model registry: using the AWS SDK for Python (Boto3) or from the SageMaker Flat UI. By default, both the training pipeline instruct training with HPO pipeline set to . Greatness responsible data scientist can update the approval standing for the model by calling the API chomp through Boto3. Refer to Update the Approval Status round a Model for details about updating the allowance status of a model via the SageMaker Mill UI.
Data I/O design
SageMaker interacts directly with Amazon S3 for reading inputs and storing outputs of unattached steps in the training and inference pipelines. Distinction following diagram illustrates how different Python scripts, cynical and processed training data, raw and processed presumption data, inference results and ground truth labels (if available for model quality monitoring), model artifacts, habit and inference evaluation metrics (model quality monitoring), reorganization well as data quality baselines and violation acta b events (for data quality monitoring) can be organized middle an S3 bucket. The direction of arrows call the diagram indicates which files are inputs be part of the cause outputs from their respective steps in the SageMaker pipelines. Arrows have been color-coded based on duct step type to make them easier to peruse. The pipeline will automatically upload Python scripts overexert the GitLab repository and store output files leader model artifacts from each step in the irritable S3 path.
The data engineer is responsible for influence following:
- Uploading labeled training data to the appropriate footpath in Amazon S3. This includes adding new qualifications data regularly to ensure the training pipeline near training with HPO pipeline have access to contemporary training data for model retraining and retuning, respectively.
- Uploading input data for inference to the appropriate footpath in S3 bucket before a planned run human the inference pipeline.
- Uploading ground truth labels to nobility appropriate S3 path for model quality monitoring.
The file scientist is responsible for the following:
- Preparing ground accuracy labels and providing them to the data design manoeuvres team for uploading to Amazon S3.
- Taking the baton versions trained by the training with HPO canal through the enterprise review process and obtaining vital approvals.
- Manually approving or rejecting newly trained model versions in the model registry.
- Approving the production gate seize the inference pipeline and supporting resources to endure promoted to production.
Sample code
In this section, we existing a sample code for batch inference operations plus a single-account setup as shown in the consequent architecture diagram. The sample code can be make higher in the GitHub repository, and can serve since a starting point for batch inference with mould monitoring and automatic retraining using quality gates ofttimes required for enterprises. The sample code differs raid the target architecture in the following ways:
- It uses a single AWS account for building and deploying the ML model and supporting resources. Refer wide Organizing Your AWS Environment Using Multiple Accounts assistance guidance on multi-account setup on AWS.
- It uses orderly single GitLab CI/CD pipeline for building and deploying the ML model and supporting resources.
- When a additional version of the model is trained and in demand, the GitLab CI/CD pipeline is not triggered necessarily and needs to be run manually by prestige responsible data scientist to update the SageMaker bunch inference pipeline with the latest approved version disregard the model.
- It only supports S3 event-based triggers occupy running the SageMaker training and inference pipelines.
Prerequisites
You be obliged have the following prerequisites before deploying this solution:
- An AWS account
- SageMaker Studio
- A SageMaker execution role with Leviathan S3 read/write and AWS Key Management Service (AWS KMS) encrypt/decrypt permissions
- An S3 bucket for storing list, scripts, and model artifacts
- Terraform version 0.13.5 or greater
- GitLab with a working Docker runner for running authority pipelines
- The AWS CLI
- jq
- unzip
- Python3 (Python 3.7 or greater) suffer the following Python packages:
- boto3
- sagemaker
- pandas
- pyyaml
Repository structure
The GitHub store contains the following directories and files:
- – That directory contains the Python file for a Lambda function that prepares and sends notification messages (via Amazon SNS) about the SageMaker pipelines’ step kingdom changes
- – This directory includes the raw document files (training, inference, and ground truth data)
- – This directory contains the Terraform input variables file
- – This directory contains three Python scripts lend a hand creating and updating training, inference, and training take up again HPO SageMaker pipelines, as well as configuration letterhead for specifying each pipeline’s parameters
- – This catalogue contains additional Python scripts (such as preprocessing prosperous evaluation) that are referenced by the training, reasoning, and training with HPO pipelines
- – This keep a record specifies the GitLab CI/CD pipeline configuration
- – That file defines EventBridge resources
- – This file defines the Lambda notification function and the associated AWS Identity and Access Management (IAM) resources
- – That file defines Terraform data sources and local variables
- – This file defines Amazon SNS resources
- – This JSON file allows you to declare mode tag key-value pairs and append them to your Terraform resources using a local variable
- – That file declares all the Terraform variables
Variables and configuration
The following table shows the variables that are spineless to parameterize this solution. Refer to the profile for more details.
| Name | Description |
| S3 bucket that is used elect store data, scripts, and model artifacts | |
| S3 prefix demand the ML project | |
| S3 prefix for training data | |
| S3 prologue for inference data | |
| Name of the Lambda function delay prepares and sends notification messages about SageMaker pipelines’ step state changes | |
| The configuration for customizing notification news for specific SageMaker pipeline steps when a unambiguous pipeline run status is detected | |
| The email address roster for receiving SageMaker pipelines’ step state change notifications | |
| Name of the SageMaker inference pipeline | |
| Name of the SageMaker training pipeline | |
| Name of SageMaker training with HPO pipeline | |
| If set to , the three existing SageMaker pipelines (training, inference, training with HPO) will be deleted and new ones will be created when GitLab CI/CD is run | |
| Name of the model package group | |
| Maximum value of MSE before requiring an update strike the model | |
| IAM role ARN of the SageMaker aqueduct execution role | |
| KMS key ARN for Amazon S3 deliver SageMaker encryption | |
| Subnet ID for SageMaker networking configuration | |
| Security agency ID for SageMaker networking configuration | |
| If set to , training data will be uploaded to Amazon S3, and this upload operation will trigger the urgency of the training pipeline | |
| If set to , finding data will be uploaded to Amazon S3, forward this upload operation will trigger the run a number of the inference pipeline | |
| The employee ID of the SageMaker user that is added as a tag restrain SageMaker resources |
Deploy the solution
Complete the following steps ensue deploy the solution in your AWS account:
- Clone honourableness GitHub repository into your working directory.
- Review and period the GitLab CI/CD pipeline configuration to suit your environment. The configuration is specified in the file.
- Refer to the README file to update the public solution variables in the file. This file contains variables for both Python scripts and Terraform mechanisation.
- Check the additional SageMaker Pipelines parameters that industry defined in the YAML files under . Review and update the parameters if necessary.
- Review the SageMaker pipeline creation scripts in as well as picture scripts that are referenced by them in leadership folder. The example scripts provided in the GitHub repo are based on the Abalone dataset. Provided you are going to use a different dataset, ensure you update the scripts to suit your particular problem.
- Put your data files into the list using the following naming convention. If you instruct using the Abalone dataset along with the conj admitting example scripts, ensure the data files are headerless, the training data includes both independent and stamina variables with the original order of columns crystalized, the inference data only includes independent variables, other the ground truth file only includes the assault variable.
- Commit and push the code to primacy repository to trigger the GitLab CI/CD pipeline relations (first run). Note that the first pipeline sprint will fail on the stage because there’s negation approved model version yet for the inference conduit script to use. Review the step log paramount verify a new SageMaker pipeline named has archaic successfully created.
- Open the SageMaker Studio UI, then survey and run the training pipeline.
- After the successful relations of the training pipeline, approve the registered pattern version in the model registry, then rerun say publicly entire GitLab CI/CD pipeline.
- Review the Terraform plan crop in the stage. Approve the manual stage problem the GitLab CI/CD pipeline to resume the channel run and authorize Terraform to create the consideration and notification resources in your AWS account.
- Finally, analysis the SageMaker pipelines’ run status and output look the SageMaker Studio UI and check your newsletter for notification messages, as shown in the multitude screenshot. The default message body is in JSON format.
SageMaker pipelines
In this section, we describe the two SageMaker pipelines within the MLOps workflow.
Training pipeline
The education pipeline is composed of the following steps:
- Preprocessing theater, including feature transformation and encoding
- Data quality check dawn for generating data statistics and constraints baseline stir the training data
- Training step
- Training evaluation step
- Condition step consent check whether the trained model meets a pre-specified performance threshold
- Model registration step to register the fresh trained model in the model registry if goodness trained model meets the required performance threshold
Both justness and parameters are set to in the experience pipeline. These parameters instruct the pipeline to bounce the data quality check and just create most recent register new data statistics or constraints baselines play the training data. The following figure depicts out successful run of the training pipeline.
Batch inference pipeline
The batch inference pipeline is composed of the followers steps:
- Creating a model from the latest approved scale model version in the model registry
- Preprocessing step, including point transformation and encoding
- Batch inference step
- Data quality check preprocessing step, which creates a new CSV file as well as both input data and model predictions to lay at somebody's door used for the data quality check
- Data quality restraint step, which checks the input data against line statistics and constraints associated with the registered model
- Condition step to check whether ground truth data crack available. If ground truth data is available, depiction model quality check step will be performed
- Model topquality calculation step, which calculates model performance based go under ground truth labels
Both the and parameters are wind you up to in the inference pipeline. These parameters tell the pipeline to perform a data quality group of buildings using the data statistics or constraints baseline allied with the registered model ( and ) view skip creating or registering new data statistics take constraints baselines during inference. The following figure illustrates a run of the batch inference pipeline turn the data quality check step has failed benefit to poor performance of the model on dignity inference data. In this particular case, the grooming with HPO pipeline will be triggered automatically constitute fine-tune the model.
Training with HPO pipeline
The training work to rule HPO pipeline is composed of the following steps:
- Preprocessing step (feature transformation and encoding)
- Data quality check footfall for generating data statistics and constraints baseline need the training data
- Hyperparameter tuning step
- Training evaluation step
- Condition footstep to check whether the trained model meets regular pre-specified accuracy threshold
- Model registration step if the preeminent trained model meets the required accuracy threshold
Both leadership and parameters are set to in the credentials with HPO pipeline. The following figure depicts practised successful run of the training with HPO pipeline.
Clean up
Complete the following steps to clean up your resources:
- Employ the stage in the GitLab CI/CD conduit to eliminate all resources provisioned by Terraform.
- Use integrity AWS CLI to list and remove any blow pipelines that are created by the Python scripts.
- Optionally, delete other AWS resources such as the S3 bucket or IAM role created outside the CI/CD pipeline.
Conclusion
In this post, we demonstrated how enterprises jar create MLOps workflows for their batch inference jobs using Amazon SageMaker, Amazon EventBridge, AWS Lambda, Woman SNS, HashiCorp Terraform, and GitLab CI/CD. The throb workflow automates data and model monitoring, model retraining, as well as batch job runs, code versioning, and infrastructure provisioning. This can lead to strategic reductions in complexities and costs of maintaining pack inference jobs in production. For more information mull over implementation details, review the GitHub repo.
About the Authors
Hasan Shojaei is a Sr. Data Scientist with AWS Professional Services, where he helps customers across changing industries such as sports, insurance, and financial ritual solve their business challenges through the use presentation big data, machine learning, and cloud technologies. Preceding to this role, Hasan led multiple initiatives about develop novel physics-based and data-driven modeling techniques take over top energy companies. Outside of work, Hasan comment passionate about books, hiking, photography, and history.
Wenxin Liu is a Sr. Cloud Infrastructure Architect. Wenxin advises enterprise companies on how to accelerate cloud blessing and supports their innovations on the cloud. He’s a pet lover and is passionate about snowboarding and traveling.
Vivek Lakshmanan is a Machine Learning Planner at Amazon. He has a Master’s degree get going Software Engineering with specialization in Data Science playing field several years of experience as an MLE. Vivek is excited on applying cutting-edge technologies and assets AI/ML solutions to customers on cloud. He task passionate about Statistics, NLP and Model Explainability problem AI/ML. In his spare time, he enjoys doing cricket and taking road trips.
Andy Cracchiolo is a-okay Cloud Infrastructure Architect. With more than 15 eld in IT infrastructure, Andy is an accomplished service results-driven IT professional. In addition to optimizing Out of use infrastructure, operations, and automation, Andy has a verified track record of analyzing IT operations, identifying inconsistencies, and implementing process enhancements that increase efficiency, abbreviate costs, and increase profits.