# KodeKloud: Beta Courses AWS Certified Machine Learning Engineer - Associate

## AWS Certified Machine Learning Engineer - Associate

### Prerequisites

- [Introduction](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Prerequisites/Introduction/page.md): Overview of an AWS Machine Learning Associate course teaching production-ready ML on AWS, covering data processing, model development, deployment, monitoring, security, and exam-focused hands-on labs.
- [Course Overview](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Prerequisites/Course-Overview/page.md): Overview of an AWS-focused machine learning engineering course covering end-to-end ML lifecycle, hands-on AWS labs, MLOps, and preparation for associate level certification exams.
- [Why AWS Machine Learning Engineer Associate Certification](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Prerequisites/Why-AWS-Machine-Learning-Engineer-Associate-Certification/page.md): Benefits of obtaining the AWS Machine Learning Engineer Associate certification, covering career growth, AWS services, exam preparation, and hands-on cloud ML skills.
- [Demo RegisteringTaking an Exam for the First Time What to know](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Prerequisites/Demo-RegisteringTaking-an-Exam-for-the-First-Time-What-to-know/page.md): Step-by-step guide to purchasing a Pearson VUE voucher and scheduling an AWS certification exam via CertMetrics, covering delivery choices, OnVUE requirements, and voucher application.
- [Demo Machine Learning Engineer Associate Exam Guide What to focus on](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Prerequisites/Demo-Machine-Learning-Engineer-Associate-Exam-Guide-What-to-focus-on/page.md): Guide on using the AWS Certified Machine Learning Engineer Associate exam guide to plan study priorities, domain weights, required AWS services, and hands on practice

### Data Preparation for Machine Learning (ML)

- [Section Introduction](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Section-Introduction/page.md): Data preparation for machine learning using a space mission analogy covering collection, cleaning, transformation, validation, and feature engineering
- [Importance of Data Preparation in Machine Learning](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Importance-of-Data-Preparation-in-Machine-Learning/page.md): The importance of data preparation for reliable machine learning, covering cleaning, transformation, feature engineering, labeling, validation, and techniques to improve model accuracy and reduce bias.
- [Overview of AWS Data Services for ML](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Overview-of-AWS-Data-Services-for-ML/page.md): Overview of AWS services for building end-to-end machine learning data pipelines including ingestion, storage, preparation, labeling, feature management, training, and orchestration.
- [Data Formats for Machine Learning](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Data-Formats-for-Machine-Learning/page.md): Practical overview of data formats for machine learning pipelines with format recommendations for analytics, training, media, and AWS services
- [Core AWS Data Sources S3 EFS FSx](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Core-AWS-Data-Sources-S3-EFS-FSx/page.md): Overview of AWS storage services S3, EFS, and FSx and guidance on their features, use cases, and selection for scalable machine learning data ingestion, processing, and training workflows
- [Demo Setting up S3 Buckets for ML Data Lakes](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Demo-Setting-up-S3-Buckets-for-ML-Data-Lakes/page.md): Guide to create and configure Amazon S3 buckets and prefix structure for machine learning data lakes, including naming, access, encryption, uploading sample data, and enabling versioning.
- [Streaming Data Sources Kinesis Flink Kafka](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Streaming-Data-Sources-Kinesis-Flink-Kafka/page.md): Comparing AWS Kinesis, Amazon Managed Flink, and Apache Kafka/MSK for building real-time streaming pipelines and ML feature processing, detailing components, trade-offs, and integration options.
- [Demo Using Kinesis for Real time Data Ingestion](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Demo-Using-Kinesis-for-Real-time-Data-Ingestion/page.md): Demonstration of using Amazon SageMaker Data Wrangler to import, inspect, clean, and export tabular datasets from S3 for machine learning workflows
- [Introduction to Data Transformation and Cleaning](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Introduction-to-Data-Transformation-and-Cleaning/page.md): Overview of data cleaning and transformation techniques for preparing datasets for reliable machine learning, including AWS tools and practical examples.
- [Feature Engineering Techniques](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Feature-Engineering-Techniques/page.md): Guide to transforming raw data into informative machine learning features, covering techniques, benefits, examples, and production tooling for scalable, validated feature engineering
- [Encoding Techniques for ML Data](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Encoding-Techniques-for-ML-Data/page.md): Overview of categorical encoding techniques for machine learning, their pros and cons, selection guidance, leakage precautions, and AWS tools for implementing and operationalizing encoding workflows
- [AWS Glue and Glue DataBrew for Data Transformation](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/AWS-Glue-and-Glue-DataBrew-for-Data-Transformation/page.md): Overview of AWS Glue and Glue DataBrew for scalable ETL and visual data preparation to clean, transform, and catalog datasets for analytics and machine learning pipelines
- [Demo AWS Glue ETL Jobs for ML Data Preparation](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Demo-AWS-Glue-ETL-Jobs-for-ML-Data-Preparation/page.md): Demonstrates using AWS Glue Studio Visual ETL to transform Titanic CSV data, change schema, drop columns, and write processed CSV to S3 for ML workflows
- [Working with SageMaker Feature Store](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Working-with-SageMaker-Feature-Store/page.md): Overview of Amazon SageMaker Feature Store explaining centralized feature management, feature groups, online and offline stores, and best practices ensuring feature reuse and consistency across ML training
- [Demo Creating and Managing Features in SageMaker Feature Store](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Demo-Creating-and-Managing-Features-in-SageMaker-Feature-Store/page.md): Guide to creating and managing Amazon SageMaker Feature Store feature groups, covering configuration, storage options, identifiers, ingestion, and best practices for online and offline feature usage.
- [Data Quality and Validation](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Data-Quality-and-Validation/page.md): Overview of data quality and validation for machine learning, covering quality dimensions, validation techniques, common issues, drift detection, and mitigation practices for reliable, fair, and robust models
- [Pre training Bias Metrics for Different Data Types](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Pre-training-Bias-Metrics-for-Different-Data-Types/page.md): Guidance on detecting and mitigating dataset bias before training, covering bias types, metrics for categorical numerical text and image data, and mitigation strategies to improve fairness.
- [Strategies for Addressing Class Imbalance](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Strategies-for-Addressing-Class-Imbalance/page.md): Overview of methods to address class imbalance in machine learning, covering resampling like oversampling and SMOTE, algorithmic weighting and cost-sensitive learning, evaluation metrics and best practices
- [SageMaker Clarify for Bias Detection](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/SageMaker-Clarify-for-Bias-Detection/page.md): Overview of SageMaker Clarify's tools to detect, measure, and explain dataset and model bias using pre and post training checks, SHAP explanations, metrics, and reports.
- [Demo Detecting and Mitigating Bias with SageMaker Clarify](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Demo-Detecting-and-Mitigating-Bias-with-SageMaker-Clarify/page.md): Demo of detecting and mitigating dataset bias using Amazon SageMaker Clarify in Data Wrangler, illustrated with a Titanic example and mitigation techniques like over and undersampling.
- [Data Labeling with SageMaker Ground Truth](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Data-Labeling-with-SageMaker-Ground-Truth/page.md): Overview of Amazon SageMaker Ground Truth for scalable data labeling using auto-labeling, active learning, human review, workforce options, and AWS integrations to produce high quality training datasets.
- [Demo Setting up a Labeling Job with SageMaker Ground Truth](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Demo-Setting-up-a-Labeling-Job-with-SageMaker-Ground-Truth/page.md): Guide to creating and launching an image labeling job in Amazon SageMaker Ground Truth including S3 setup, IAM roles, workforce selection, and labeling interface configuration.
- [Data Security Considerations for ML](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Data-Security-Considerations-for-ML/page.md): Securing machine learning data and pipelines on AWS by protecting datasets, preventing poisoning, enforcing encryption and access controls, and monitoring deployments for privacy and integrity
- [Data Encryption Techniques for ML Datasets](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Data-Encryption-Techniques-for-ML-Datasets/page.md): Overview of encryption strategies and AWS controls for protecting machine learning datasets at rest, in transit, and during processing with KMS and best practices.
- [Data Classification Anonymization and Masking](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Data-Classification-Anonymization-and-Masking/page.md): Protecting sensitive data in ML pipelines through classification, anonymization, masking, and access controls using AWS tools like Macie, Glue, and SageMaker.
- [Compliance Requirements for ML Data PII PHI Data Residency](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Data-Preparation-for-Machine-Learning-ML/Compliance-Requirements-for-ML-Data-PII-PHI-Data-Residency/page.md): Overview of compliance considerations and AWS tools for protecting sensitive ML data such as PII PHI and financial information, covering residency, access control, encryption, and monitoring.

### ML Model Development

- [Section Introduction](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Section-Introduction/page.md): Framework mapping ML model development phases on AWS to a space mission analogy, covering design, assembly, testing, deployment, monitoring, and post-deployment review
- [Introduction to ML Model Development on AWS](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Introduction-to-ML-Model-Development-on-AWS/page.md): Overview of AWS SageMaker for end to end ML lifecycle including data preparation, model building, training and tuning, deployment, monitoring, and automated pipelines
- [ML Algorithms and Business Problem Mapping](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/ML-Algorithms-and-Business-Problem-Mapping/page.md): Guide to mapping business problems to appropriate ML paradigms, selecting algorithms and metrics, and deploying interpretable, actionable models
- [AWS AI Services Overview](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/AWS-AI-Services-Overview/page.md): Overview of AWS AI services for language speech and vision, covering Translate Rekognition Transcribe and Polly, use cases, features, and common integration patterns
- [Model Interpretability Considerations](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Model-Interpretability-Considerations/page.md): Overview of machine learning interpretability, why it matters, and practical techniques like SHAP, LIME, feature importance, and PDPs with guidance for application and production
- [SageMaker Built in Algorithms](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/SageMaker-Built-in-Algorithms/page.md): Overview of Amazon SageMaker built-in algorithms, their use cases, supported data types, benefits, and example usage for scalable training, model families like tabular, NLP, vision, time-series, and unsupervised
- [Demo Exploring SageMaker Built in Algorithms](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Demo-Exploring-SageMaker-Built-in-Algorithms/page.md): Guide to using AWS SageMaker JumpStart to discover, evaluate, deploy, and fine-tune prebuilt machine learning models while managing costs and cleaning up resources
- [Foundation Models and SageMaker JumpStart](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Foundation-Models-and-SageMaker-JumpStart/page.md): Overview of foundation models and using Amazon SageMaker JumpStart to browse, deploy, and fine tune large pretrained models with deployment, cost, and best practice guidance
- [Demo Using SageMaker JumpStart for Quick Model Development](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Demo-Using-SageMaker-JumpStart-for-Quick-Model-Development/page.md): How to quickly develop, customize, fine tune and deploy models using Amazon SageMaker JumpStart with one click deployment, Studio UI or AI agents and endpoint configuration
- [ML Training Process](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/ML-Training-Process/page.md): Overview of machine learning training processes, including data splitting, training loops, loss functions, optimizers, gradient descent variants, and common terminology for model development and evaluation
- [Methods to Reduce Model Training Time](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Methods-to-Reduce-Model-Training-Time/page.md): Practical strategies to shorten machine learning training time through data preprocessing, hyperparameter tuning, optimizers, infrastructure choices, Spot instances, and distributed training on AWS SageMaker.
- [Regularization Techniques in ML](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Regularization-Techniques-in-ML/page.md): Overview of regularization methods to reduce overfitting and improve generalization in machine learning, covering L1 L2 Elastic Net dropout early stopping and SageMaker usage.
- [Hyperparameter Tuning Techniques](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Hyperparameter-Tuning-Techniques/page.md): Overview of hyperparameter tuning methods, pros and cons, and managed tools like SageMaker for optimizing machine learning model performance and resource use.
- [Using SageMaker Script Mode with Frameworks](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Using-SageMaker-Script-Mode-with-Frameworks/page.md): Guide to using Amazon SageMaker Script Mode to run existing training scripts in prebuilt framework containers for managed scalable training, experiment tracking, and easy deployment.
- [Fine Tuning Pre Trained Models](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Fine-Tuning-Pre-Trained-Models/page.md): Guide to fine-tuning pre-trained models for domain-specific tasks, workflows, SageMaker fine tuning options, modes like LoRA and full tuning, and best practices for training and deployment
- [Demo Fine tuning a Pre trained Model with SageMaker JumpStart](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Demo-Fine-tuning-a-Pre-trained-Model-with-SageMaker-JumpStart/page.md): Guide to fine-tuning pre-trained models using AWS SageMaker JumpStart, covering artifacts, compute, hyperparameters, training workflow, and LoRA customization
- [Ensemble Learning Methods](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Ensemble-Learning-Methods/page.md): Overview of ensemble learning methods—bagging, boosting, and stacking— their principles, advantages, and practical examples for improving model accuracy, robustness, and generalization
- [Model Size Reduction Techniques](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Model-Size-Reduction-Techniques/page.md): Methods to reduce large language model size and optimize deployments using pruning, quantization, distillation, and low-rank factorization for edge, mobile, and low-resource environments.
- [Demo Model Version Management with SageMaker Model Registry](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Demo-Model-Version-Management-with-SageMaker-Model-Registry/page.md): Guide to Amazon SageMaker Model Registry for versioning models, tracking metadata and evaluations, and enforcing approval workflows for reproducible, auditable deployments.
- [Model Evaluation Techniques and Metrics](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Model-Evaluation-Techniques-and-Metrics/page.md): Overview of machine learning model evaluation techniques, metrics, validation strategies, and monitoring practices to assess and compare model performance for classification and regression tasks
- [Creating Performance Baselines](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Creating-Performance-Baselines/page.md): Guide on creating and using baseline models to evaluate machine learning performance, from naive and heuristic to model-based and SOTA baselines, with examples and a practical checklist.
- [Shadow Deployment for Model Evaluation](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Model-Development/Shadow-Deployment-for-Model-Evaluation/page.md): Shadow deployment strategy for validating candidate ML models by mirroring production traffic to collect telemetry and evaluate performance without impacting users.

### Deployment and Orchestration of ML Workflows

- [Section Introduction](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Section-Introduction/page.md): Overview of MLOps comparing model deployment to satellite launches, covering CI/CD mapping, orchestration, monitoring, drift detection, reproducibility, and safe rollouts
- [Introduction to ML Deployment and Orchestration](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Introduction-to-ML-Deployment-and-Orchestration/page.md): Guide to deploying and orchestrating ML models on AWS covering SageMaker deployment patterns, packaging, CI CD, monitoring, model registry, and automated retraining
- [ML Deployment Best Practices](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/ML-Deployment-Best-Practices/page.md): Best practices for production-ready ML deployment focusing on reliability, security, cost optimization, testing, monitoring, automation, environment isolation, and gradual rollouts
- [AWS Deployment Services for ML](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/AWS-Deployment-Services-for-ML/page.md): Overview of AWS SageMaker deployment patterns, model packaging, CI/CD, monitoring, and infrastructure choices for scalable machine learning serving
- [Real time vs Batch Inference](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Real-time-vs-Batch-Inference/page.md): Comparing real-time and batch ML inference on AWS SageMaker, covering deployment options, scaling, monitoring, cost optimization, CI/CD, model versioning, and security best practices.
- [Demo Deploying a Model for Real time Inference](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Demo-Deploying-a-Model-for-Real-time-Inference/page.md): Guide to deploying and managing AWS SageMaker real time inference endpoints, covering serverless versus provisioned hosting, model variants, encryption, invocation, monitoring, and operational best practices.
- [Demo Setting up Batch Transform Jobs](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Demo-Setting-up-Batch-Transform-Jobs/page.md): Tutorial for configuring and launching Amazon SageMaker Batch Transform jobs to run offline inference on S3 datasets, covering instance choices, input and output setup, monitoring, and cost optimization.
- [SageMaker Endpoint Types Serverless Real time Asynchronous](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/SageMaker-Endpoint-Types-Serverless-Real-time-Asynchronous/page.md): Overview comparing Amazon SageMaker endpoint types and trade offs across serverless, real time, multi model, and asynchronous deployments for latency, cost, scalability, monitoring, and security
- [Container Options for ML Deployment](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Container-Options-for-ML-Deployment/page.md): Overview of container strategies for deploying machine learning on AWS covering SageMaker prebuilt images, BYOC, managed platforms, GPU compatibility, security, image optimization, and cost versus operational tradeoffs
- [Demo Creating Custom Containers for SageMaker](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Demo-Creating-Custom-Containers-for-SageMaker/page.md): Guide to registering and creating Amazon SageMaker models from existing ECR container images and S3 model artifacts via the console, including required fields, VPC and IAM permissions.
- [Edge Deployment with SageMaker Neo](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Edge-Deployment-with-SageMaker-Neo/page.md): Deploying and optimizing trained machine learning models for resource constrained edge devices using AWS SageMaker Neo, including compilation, packaging, OTA updates, monitoring, and hardware specific optimization.
- [On Demand vs Provisioned Resources for ML](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/On-Demand-vs-Provisioned-Resources-for-ML/page.md): Guide comparing on-demand versus provisioned cloud resources for ML, explaining trade-offs and hybrid strategies to balance cost, reliability, and scalability
- [Infrastructure as Code Options for ML Workloads](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Infrastructure-as-Code-Options-for-ML-Workloads/page.md): Overview of Infrastructure as Code options and best practices for provisioning, deploying, securing, and operating AWS machine learning environments using CloudFormation, CDK, and the AWS CLI
- [Containerization for ML Workloads](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Containerization-for-ML-Workloads/page.md): Containerizing machine learning workloads on AWS covering Docker packaging, deployment on SageMaker ECS EKS or Lambda, security best practices, monitoring, and cost optimization
- [Auto Scaling SageMaker Endpoints](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Auto-Scaling-SageMaker-Endpoints/page.md): How to automatically scale Amazon SageMaker endpoints using CloudWatch and Application Auto Scaling to balance latency, cost, security, and observability
- [VPC Configuration for SageMaker](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/VPC-Configuration-for-SageMaker/page.md): Guide to configuring Amazon SageMaker within a VPC to secure network traffic, use VPC endpoints, manage security groups, IAM, KMS, and monitor access for private training and hosting
- [CICD Principles for ML Workflows](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/CICD-Principles-for-ML-Workflows/page.md): Principles for CI/CD in machine learning workflows automating versioned code data and models with testing validation secure deployment monitoring and retraining to ensure reproducible reliable production rollouts
- [AWS CodePipeline CodeBuild and CodeDeploy for ML](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/AWS-CodePipeline-CodeBuild-and-CodeDeploy-for-ML/page.md): Automating MLOps CI/CD on AWS using CodePipeline, CodeBuild, SageMaker, and CodeDeploy with secure practices, testing, model registry, and safe rollout strategies.
- [Version Control Systems for ML Projects](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Version-Control-Systems-for-ML-Projects/page.md): Best practices for versioning code, datasets, and models in ML projects using AWS services, CI/CD pipelines, and IAM to ensure reproducibility, traceability, and secure deployments
- [Deployment Strategies and Rollback Actions](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Deployment-Strategies-and-Rollback-Actions/page.md): Overview of ML deployment strategies, automated rollback mechanisms, AWS tooling, security, monitoring, disaster recovery, and best practices for safe reproducible production MLOps.
- [Automating Data Ingestion with Orchestration Services](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Automating-Data-Ingestion-with-Orchestration-Services/page.md): Guide to automating ML and analytics data ingestion on AWS using Glue, Step Functions, EventBridge, and Lambda, covering architectures, event-driven patterns, monitoring, CI/CD, and best practices.
- [Demo Setting up SageMaker Pipelines](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Demo-Setting-up-SageMaker-Pipelines/page.md): Guide to building, configuring, and managing end-to-end, cost-aware SageMaker Pipelines using Studio's visual editor for processing, training, model registry, checkpointing, and deployment.
- [Implementing Automated Tests in ML Pipelines](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Deployment-and-Orchestration-of-ML-Workflows/Implementing-Automated-Tests-in-ML-Pipelines/page.md): Guidance on embedding automated tests into ML pipelines, covering unit integration and end to end tests, AWS CI CD patterns, security monitoring backups and best practices

### ML Solution Monitoring, Maintenance, and Security

- [Section Introduction](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Section-Introduction/page.md): Guide to monitoring, automating maintenance, security, and cost optimization of production ML systems on AWS using SageMaker, CloudWatch, EventBridge, and CI/CD pipelines
- [Understanding Drift in ML Models](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Understanding-Drift-in-ML-Models/page.md): Explains data and concept drift, impacts on model performance, and AWS detection and mitigation patterns using SageMaker Model Monitor, CloudWatch, automated retraining, and CI/CD
- [Model Performance Monitoring Techniques](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Model-Performance-Monitoring-Techniques/page.md): Monitoring and maintaining ML model performance on AWS using SageMaker, CloudWatch, CI/CD, automation, drift detection, alerting, and secure retraining pipelines.
- [Using SageMaker Model Monitor](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Using-SageMaker-Model-Monitor/page.md): Guide to using Amazon SageMaker Model Monitor to detect data drift, monitor model quality, and automate alerts plus CI/CD-driven retraining and deployment
- [Demo Setting up Model Monitoring with SageMaker](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Demo-Setting-up-Model-Monitoring-with-SageMaker/page.md): Guide to enabling and configuring Amazon SageMaker Model Monitor for real-time endpoints, including deployment, data capture to S3, baseline setup, scheduling monitoring jobs, and viewing results.
- [Detecting Data Drift with SageMaker Clarify](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Detecting-Data-Drift-with-SageMaker-Clarify/page.md): Guide to detecting and automating remediation of ML data drift using SageMaker Clarify, CloudWatch, Lambda, and CI/CD to monitor, alert, retrain, and maintain model reliability.
- [Demo Implementing Data Drift Detection](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Demo-Implementing-Data-Drift-Detection/page.md): Guide to detecting and monitoring data drift with Amazon SageMaker Model Monitor, creating baselines, running statistical checks, and responding to drift in production.
- [Monitoring Workflows for Anomalies](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Monitoring-Workflows-for-Anomalies/page.md): Guide to building AWS end-to-end anomaly monitoring for ML workflows using CloudWatch, SageMaker, Lambda, and CodePipeline with dashboards, automation, IaC, and security best practices.
- [AB Testing for Model Performance Monitoring](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/AB-Testing-for-Model-Performance-Monitoring/page.md): Describes A/B testing to monitor and compare ML model variants in production using AWS SageMaker, CloudWatch, Model Monitor, plus CI/CD, security, auto scaling, and cost optimization.
- [Demo Setting up AB Testing with SageMaker](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Demo-Setting-up-AB-Testing-with-SageMaker/page.md): Guide to deploying a second XGBoost model in SageMaker, configuring endpoint variants for A/B and shadow testing, enabling data capture, and using Model Monitor for evaluation and drift detection
- [Key Performance Metrics for ML Infrastructure](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Key-Performance-Metrics-for-ML-Infrastructure/page.md): Overview of ML infrastructure monitoring, key performance metrics, CI/CD integration, auto scaling, security, and cost optimization for production models
- [Monitoring Tools for ML Systems](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Monitoring-Tools-for-ML-Systems/page.md): Overview of AWS monitoring tools and operational patterns for managing production ML systems including drift detection, alerts, CI CD automation, responsible AI checks, security, autoscaling, and cost optimization
- [Demo Using CloudWatch for ML Infrastructure Monitoring](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Demo-Using-CloudWatch-for-ML-Infrastructure-Monitoring/page.md): Using AWS CloudWatch to diagnose SageMaker real-time endpoint failures by inspecting logs, metrics, and alarms to identify packaging, import, or resource issues and guide remediation.
- [Using CloudTrail for ML Activity Logging](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Using-CloudTrail-for-ML-Activity-Logging/page.md): Using AWS CloudTrail with S3, Athena, CloudWatch, EventBridge and Lambda to log, monitor, analyze, secure, and automate ML activity such as Amazon SageMaker
- [ML Specific Instance Types and Performance Considerations](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/ML-Specific-Instance-Types-and-Performance-Considerations/page.md): Guide to selecting and optimizing AWS ML instance types, covering performance, monitoring, autoscaling, security, and cost optimization for training and inference.
- [Demo Using AWS Cost Explorer for ML Cost Tracking](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Demo-Using-AWS-Cost-Explorer-for-ML-Cost-Tracking/page.md): Guide to using AWS Cost Explorer to track, analyze, and forecast ML demo and development costs while optimizing spend with tagging, budgets, and resource cleanup
- [Rightsizing ML Instances with SageMaker Inference Recommender](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Rightsizing-ML-Instances-with-SageMaker-Inference-Recommender/page.md): Benchmark and select optimal ML inference instances with SageMaker Inference Recommender to balance performance, cost, deployment, monitoring, and security.
- [Demo Using SageMaker Inference Recommender](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Demo-Using-SageMaker-Inference-Recommender/page.md): Guide to using SageMaker Inference Recommender to automate benchmarking across instance types, compare latency, throughput, and cost, and provide recommendations for production model deployment.
- [Optimizing Infrastructure Costs with Spot Instances](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Optimizing-Infrastructure-Costs-with-Spot-Instances/page.md): Practical guide to reducing AWS cloud compute costs using Spot Instances with SageMaker, checkpointing, monitoring, interruption handling, and cost analysis.
- [IAM Roles Policies and Groups for ML Services](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/IAM-Roles-Policies-and-Groups-for-ML-Services/page.md): Managing IAM roles, policies, and groups to secure AWS machine learning workflows like SageMaker with least privilege, monitoring via CloudTrail, and encryption with KMS
- [SageMaker Security and Compliance Features](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/SageMaker-Security-and-Compliance-Features/page.md): Practical security and compliance controls for Amazon SageMaker workloads using IAM, KMS encryption, VPC isolation, CloudTrail auditing, and automated compliance to protect ML data and deployments
- [SageMaker Role Manager](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/SageMaker-Role-Manager/page.md): Overview of AWS SageMaker Role Manager for creating least-privilege IAM roles and templates to secure training, inference, and pipeline workloads with service integrations and auditing.
- [Network Access Controls for ML Resources](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Network-Access-Controls-for-ML-Resources/page.md): Guide to securing cloud ML workloads using AWS VPCs, security groups, VPC endpoints, IAM, and monitoring to protect SageMaker, S3, and EC2 resources.
- [Security Best Practices for ML CICD Pipelines](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Security-Best-Practices-for-ML-CICD-Pipelines/page.md): Practical security best practices for protecting ML CI CD pipelines and artifacts using AWS services such as IAM, KMS, VPC, CloudTrail, GuardDuty, and AWS Config.
- [Monitoring and Auditing ML Systems](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/ML-Solution-Monitoring-Maintenance-and-Security/Monitoring-and-Auditing-ML-Systems/page.md): Monitoring and auditing production ML systems on AWS using CloudWatch, CloudTrail, SageMaker Model Monitor, AWS Config, IAM and KMS to detect drift, ensure observability, security, and compliance

### Bringing it all together

- [Summary of Domain 1 Data Preparation for ML](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Bringing-it-all-together/Summary-of-Domain-1-Data-Preparation-for-ML/page.md): Overview of data preparation for machine learning, covering cleaning, transformation, feature engineering, labeling, validation, security, and AWS tools for scalable, reproducible pipelines and production-ready models.
- [Summary of Domain 2 ML Model Development](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Bringing-it-all-together/Summary-of-Domain-2-ML-Model-Development/page.md): Overview of machine learning model development covering lifecycle stages, algorithms, training and tuning, regularization, fine tuning and ensemble methods, deployment patterns, automation, and production monitoring on AWS
- [Summary of Domain 3 Deployment and Orchestration of ML Workflows](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Bringing-it-all-together/Summary-of-Domain-3-Deployment-and-Orchestration-of-ML-Workflows/page.md): Overview of deploying and orchestrating machine learning workflows on AWS, covering deployment patterns, containers, security, IaC, CI CD, edge optimization, and production monitoring
- [Summary of Domain 4 ML Solution Monitoring Maintenance and Security](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Bringing-it-all-together/Summary-of-Domain-4-ML-Solution-Monitoring-Maintenance-and-Security/page.md): Best practices for monitoring, maintaining, and securing production ML solutions on AWS, including drift detection, observability, automated remediation, A/B testing, logging, rightsizing, and compliance tools.
- [Continual Learning Resources](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Bringing-it-all-together/Continual-Learning-Resources/page.md): Guide to advancing AWS machine learning skills with certification roadmap, practical focus areas, hands-on projects, sandbox experimentation, continuous learning habits, and recommended certifications and resources
- [Specific Next Steps](https://notes.kodekloud.com/docs/AWS-Certified-Machine-Learning-Engineer-Associate/Bringing-it-all-together/Specific-Next-Steps/page.md): Concise study plan to pass the AWS Certified Machine Learning Associate exam using timed full-length practice exams, targeted quizzes, error review, and KodeKloud Discord support.