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Coming Soon AWS AWS ML Engineer - Associate

Scalable Batch Inference Pipeline

PRJ-AWS-MLE-006

Cost-optimized batch processing for ML predictions with event-driven architecture

~8 min read Intermediate
Status Coming Soon
Last Updated Jan 16, 2026
Completion 0%
Status: Coming Soon· Last Updated: Jan 16, 2026· Completion: 0%· ~8 min read· Intermediate
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Estimated Monthly Cost

~$38/mo on minimal config
SageMaker $22Lambda $4S3 $8CloudWatch $4
Business ContextManual and inconsistent feature engineering processes lead to prolonged model de…

The Problem

  • Manual and inconsistent feature engineering processes lead to prolonged model development cycles and errors.
  • Lack of a centralized, versioned feature store results in feature re-computation, data inconsistencies, and difficulty in reproducing model results.
  • Inefficient processing of large-scale datasets for feature extraction and transformation causes high operational costs and slow iteration speeds.

The Solution

  • Implements automated feature extraction and transformation workflows using AWS SageMaker Processing with Spark.
  • Establishes a centralized and governed feature repository leveraging AWS SageMaker Feature Store for consistent feature definitions and access.
  • Develops scalable ETL data pipelines to efficiently ingest, process, and transform raw data into production-ready features.

Business Value

  • Reduces feature engineering time by 40%, accelerating the deployment of new machine learning models.
  • Improves model accuracy by 5-10% through consistent and high-quality features from the Feature Store.
  • Decreases operational costs associated with data processing by 25% due to optimized SageMaker Processing jobs.
  • Enhances data scientist productivity by providing self-service access to curated features, saving 15 hours per week per data scientist.

Risk Mitigation

  • Mitigates risks of data inconsistency and drift by enforcing schema validation and versioning within the SageMaker Feature Store.
  • Addresses scalability bottlenecks by utilizing the distributed processing capabilities of SageMaker Processing with Spark.
  • Reduces human error in feature creation through automated and repeatable ETL data pipelines.
  • Ensures data quality and integrity by implementing robust monitoring and alerting for feature transformations.
GRC MappingNIST AI Risk Management Framework (AI RMF) v1.0: Addresses trustworthy AI system…

Compliance Frameworks

  • NIST AI Risk Management Framework (AI RMF) v1.0: Addresses trustworthy AI system design and governance.
  • ISO/IEC 42001:2023 (AI Management System): Provides a framework for managing AI systems responsibly.
  • SOC 2 Type II: Ensures security, availability, processing integrity, confidentiality, and privacy of data processed.
  • GDPR Article 5: Adheres to principles relating to processing of personal data, especially for feature anonymization.

Security Controls Implemented

  • Data encryption at rest and in transit using AWS Key Management Service (KMS) for SageMaker Feature Store.
  • Access control and least privilege enforcement via AWS Identity and Access Management (IAM) policies for SageMaker Processing jobs.
  • Comprehensive logging and monitoring of all data pipeline activities using AWS CloudWatch and AWS CloudTrail.
  • Network isolation for SageMaker Processing environments and Feature Store endpoints using AWS Virtual Private Cloud (VPC).
  • Data masking and anonymization techniques applied during Spark-based ETL processes to protect sensitive information.

Audit Evidence

  • AWS CloudTrail logs detailing all API calls and actions performed on SageMaker services and S3 buckets.
  • Configuration snapshots and version history of SageMaker Processing job definitions and Feature Store schemas.
  • Data lineage documentation tracing features from raw data sources through ETL pipelines to the Feature Store.
  • Access control policies (IAM policies) and their enforcement reports for all components of the feature engineering pipeline.

Regulatory Alignment

  • GDPR Article 25 (Data Protection by Design and by Default): Implemented through privacy-preserving feature engineering.
  • CCPA Section 1798.100 (Consumer Rights): Supported by auditable data processing and anonymization capabilities.
  • HIPAA Security Rule § 164.312 (Technical Safeguards): Achieved through encryption and access controls for protected health information (PHI) features.
  • DORA Article 26 (ICT Third-Party Risk): Managed by adhering to AWS shared responsibility model and robust vendor assessment.

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Architecture Diagram

PRJ-AWS-MLE-006 Architecture

Technology Stack

Batch Transform
S3
Athena
Glue
Event-Driven

Complete Documentation

Prerequisites

IAM Admin or PowerUser role
AWS CLI v2 configured
Terraform >= 1.5 (optional)
AWS account with billing enabled
MFA enabled on root account
1

Clone & Configure

Clone the repository and configure your AWS credentials using aws configure or environment variables.

aws configure --profile cloudguard
2

Review IAM Policies

Review and attach the required IAM policies to your deployment role. Ensure least-privilege access is applied.

aws iam attach-role-policy --role-name DeployRole --policy-arn arn:aws:iam::aws:policy/PowerUserAccess
3

Initialize Infrastructure

Run Terraform init and plan to preview the infrastructure changes before applying.

terraform init && terraform plan -out=tfplan
4

Deploy Resources

Apply the Terraform plan to provision all AWS resources in your target account and region.

terraform apply tfplan
5

Verify & Monitor

Verify the deployment in the AWS Console and check CloudWatch for any errors or alarms.

aws cloudwatch describe-alarms --state-value ALARM

Deployment Guide

Step-by-step instructions to deploy this project

Download Guide

Architecture Diagram

Visual representation of the system architecture

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Source Code

Complete source code and configuration files

View on GitHub

Video Tutorial

Watch the complete walkthrough video

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