MLE-C01

AWS — Associate Certification · MLE-C01 · Valid 3 Years

Machine Learning

Engineer Associate

AWS Certified Machine Learning Engineer – Associate validates the ability to implement, operationalize, and monitor ML workloads in production on AWS. It covers SageMaker, MLOps pipelines, feature engineering, model deployment, and performance optimization.

Amazon SageMaker Feature Engineering Model Training MLOps SageMaker Pipelines Model Monitoring AWS Glue Amazon S3 SageMaker Feature Store AutoML

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MLE-C01 — ML Pipeline Architecture on AWS

Topics

Full MLE-C01 scope

32+ hrs

Duration

Self-paced learning

Associate

ML Cert

Engineer focused

24/7

Support

Expert guidance

Exam Domains

Four Core ML Engineering Domains

The MLE-C01 exam validates skills across data preparation, model development, deployment, and production monitoring on AWS.

Domain 01

Data Preparation for Machine Learning

28%

exam weight

›Identify and ingest data from AWS services — S3, Glue, Kinesis, DMS
›Feature engineering — encoding, normalization, binning, dimensionality reduction
›Handle missing values, outliers, imbalanced data, and label noise
›SageMaker Data Wrangler for visual data transformation workflows
›Data labelling with SageMaker Ground Truth and automated labelling

Domain 02

ML Model Development

28%

exam weight

›Select appropriate algorithms — XGBoost, linear models, deep learning, AutoML
›Configure SageMaker training jobs with built-in and custom containers
›Hyperparameter tuning with SageMaker Automatic Model Tuning (HPO)
›Evaluate model performance — AUC, F1, RMSE, confusion matrices
›Manage model versions and approvals in the SageMaker Model Registry

Domain 03

Deployment & Orchestration

24%

exam weight

›Deploy models to real-time endpoints, batch transform, and serverless inference
›SageMaker Pipelines for end-to-end ML CI/CD workflows
›Container-based deployments using Amazon ECR and Docker
›Multi-model and multi-container endpoints for cost efficiency
›Blue/green and canary deployment strategies for model updates

Domain 04

ML Solution Monitoring

20%

exam weight

›SageMaker Model Monitor for data drift, model quality, and bias detection
›CloudWatch metrics and alarms for endpoint performance and latency
›SageMaker Clarify for explainability reports and bias analysis
›Retraining pipelines triggered by drift detection and scheduled jobs
›Cost optimization — Spot training, rightsizing instances, Savings Plans

Why MLE-C01

The Essential AWS ML Engineering Certification

ML in production requires more than model building. MLE-C01 proves you can operationalize, monitor, and continuously improve ML workloads on AWS at enterprise scale.

Associate Tier — ML Engineering Track

MLE-C01 is the bridge between data science and production engineering. It's the first AWS certification specifically targeting MLOps practitioners who deploy and operate ML workloads on SageMaker.

SageMaker Is the ML Platform

Amazon SageMaker powers the full ML lifecycle. MLE-C01 validates deep expertise in building, training, deploying, and monitoring models using SageMaker's rich feature set.

MLOps Drives Production Success

SageMaker Pipelines, Model Registry, and Model Monitor enable automated, reproducible ML workflows. MLE-C01 ensures you can build and govern these pipelines.

Security Is Built In

IAM, VPC endpoints, KMS encryption, and SageMaker execution roles are core exam topics — ensuring you build ML systems that are secure by design.

Cost Optimization Matters

Spot training, rightsizing, managed Spot training interruptions, and Savings Plans reduce ML costs significantly. MLE-C01 proves you can optimize ML spend.

Opens Doors to Senior ML Roles

MLE-C01 positions you for MLS-C01 (ML Specialty) and senior MLOps, ML engineering, and AI infrastructure roles across top-tier organizations.

Curriculum

7-Topic MLE-C01 Programme

Complete ML engineering curriculum covering data preparation, model development, deployment, and monitoring on AWS SageMaker.

Identify and access data from S3, RDS, DynamoDB, Kinesis, and Redshift
Data profiling, schema validation, and quality checks using AWS Glue Data Catalog
Handling missing values, outliers, and class imbalance strategies
Data splitting — training, validation, and test set strategies
SageMaker Data Wrangler for low-code data transformation and feature preparation

Feature encoding — one-hot, label, target, binary encoding for categorical data
Numerical feature scaling — standardization, min-max, robust normalization
Feature selection — correlation analysis, PCA, feature importance with XGBoost
Text features — TF-IDF, word embeddings, tokenization strategies
SageMaker Feature Store — online/offline store, feature groups, ingestion pipelines

Configure SageMaker training jobs — instance types, spot training, checkpointing
Built-in algorithms — XGBoost, Linear Learner, BlazingText, DeepAR, Object Detection
Bring Your Own Container (BYOC) with Amazon ECR and Docker
Distributed training strategies — data parallelism, model parallelism
SageMaker Automatic Model Tuning for hyperparameter optimization (HPO)

Regression metrics — RMSE, MAE, R-squared; Classification — AUC-ROC, F1, precision, recall
Confusion matrix interpretation and threshold optimization
Overfitting and underfitting detection — bias-variance trade-off
Cross-validation strategies for robust model evaluation
SageMaker Clarify for bias detection and model explainability reports

SageMaker real-time endpoints — instance selection, auto scaling, traffic shifting
Batch Transform for large-scale offline inference on S3 datasets
Serverless Inference for intermittent workloads — pay-per-invocation
Multi-model endpoints and multi-container endpoints for cost efficiency
SageMaker Model Registry — versioning, metadata, approval workflows, cross-account

SageMaker Managed Spot Training — checkpointing and interruption handling
Instance rightsizing — compute, memory, and GPU optimized instance selection
Savings Plans and Reserved Instances for SageMaker training and endpoints
SageMaker Pipelines for automated, reproducible ML CI/CD workflows
SageMaker Neo for model compilation and edge optimization (IoT Greengrass)

IAM roles and policies for SageMaker — execution roles, cross-account access
VPC networking for SageMaker — private endpoints, network isolation mode
Encryption at rest (KMS) and in transit (TLS) for SageMaker resources
Secrets Manager for database credentials and API keys in training scripts
Audit ML activities using AWS CloudTrail and SageMaker experiment tracking

Course Snapshot

7 Topics

Full MLE-C01 domains

32+ Hours

Total learning time

Associate Cert

ML Engineer level

Tech Support

Call / WhatsApp

Mon–Fri

9 AM – 6 PM

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Career Outcomes

MLE-C01 Careers in AI & ML

ML engineering roles are among the fastest-growing in tech. MLE-C01 proves you can move models from notebooks to production, unlocking high-value cloud AI career paths.

Infosys

ML Engineer

TCS

AI Developer

Wipro

MLOps Eng.

Accenture

Data Engineer

ML Engineer

Design, build, and operationalize ML pipelines on Amazon SageMaker, from data ingestion through model deployment and continuous monitoring.

₹10–28 LPA

MLOps Engineer

Build and maintain automated ML workflows using SageMaker Pipelines, CI/CD integrations, and model governance frameworks at scale.

₹12–30 LPA

Data Engineer (ML)

Architect data pipelines that feed ML systems — using AWS Glue, Kinesis, EMR, and SageMaker Feature Store for feature computation.

₹9–24 LPA

AI/ML Developer

Develop ML-powered applications integrating SageMaker endpoints with application backends, APIs, and real-time inference systems.

₹10–26 LPA

SageMaker Specialist

Deep expertise in SageMaker's full feature set — advising organisations on ML architecture, cost, and production readiness.

₹12–32 LPA

Topics

32+ hrs

Training Hours

Associate

Cert Level

MLE-C01

AWS Certified

Machine Learning

Engineer Associate

Four Core ML Engineering Domains

The Essential AWS ML Engineering Certification

7-Topic MLE-C01 Programme

Get a Custom Quotation

MLE-C01 Careers in AI & ML

Ready to Master AWS ML Engineering?