AWS Certified Machine Learning Engineer Associate: Hands On!
Practice exam included! Master MLA-C02/ ME1-C02 AWS Machine Learning Engineer Exam: SageMaker, Bedrock, and AI Skills.
Lo que serás capaz de hacer
FULLY UPDATED for the MLA-C02 exam released in September 2026. Practice exam included. Get certified by Amazon for your knowledge of machine learning on AWS! Prepare to ace one of the most challenging certifications in the cloud domain—the AWS Certified Machine Learning Engineer Associate Exam! Whether you're a backend developer, data engineer, or data scientist, this comprehensive course is your gateway to success. Why This Course? This course is expertly crafted by industry veterans Frank Kane and Stephane Maarek , who have collectively educated over 3 million students on Udemy. Frank Kane, with over 9 years of experience at Amazon, has specialized in machine learning and AI, and Stephane Maarek is an AWS expert and renowned instructor. Together, they bring an unparalleled depth of knowledge to guide you through every aspect of the exam. What You’ll Learn: Master AWS ML and AI Services: Dive deep into Amazon SageMaker, Amazon Bedrock, and a host of other AWS services like Comprehend, Rekognition, and Translate, which are crucial for the exam. Hands-on Labs: Gain practical experience with hands-on activities, labs, and demos that reinforce your understanding and help you build confidence. Practice Exam and Practice Questions: A full-length 65-question practice exam and 110 quiz questions throughout the course test your knowledge, in a style similar to the exam Data Preparation & Feature Engineering: Learn how to ingest, transform, and validate data for ML modeling, ensuring data integrity and model readiness. Model Development & Deployment: Explore hyperparameter tuning, model performance analysis, and best practices for deploying scalable ML and AI solutions on AWS. Monitoring & Security: Discover how to monitor ML models and infrastructure, optimize costs, and secure your AWS environment, ensuring compliance and performance. Why Choose Us? Proven Track Record: Our instructors have helped millions of students achieve their AWS certification goals. Real-World Experience: Learn from experts who have worked at Amazon and have extensive experience with AWS services. Comprehensive Coverage: This course covers everything you need to pass the exam—from AWS service knowledge to advanced machine learning topics that the exam will test you on. Who Should Enroll? This course is perfect for anyone preparing to take the AWS Certified Machine Learning Engineer Associate Exam. If you're serious about your certification and want to ensure you walk into the exam center with confidence, this course is for you. Don’t Leave Your Success to Chance This certification is tough, and the stakes are high. Don't risk hundreds of dollars on an exam until you're fully prepared. Enroll now and take the first step towards becoming an AWS Certified Machine Learning Engineer! Enroll Today and Start Your Journey to Certification Success! Don't just take our word for it - here are some real student reviews just taken from the past couple of weeks: "I took the ML Associate exam last month and cleared it because of this course!" - Ashkay "Glad I took this course, was able to pass MLA !!" - Rajendra "I just passed the exam. This course was so helpful. Thanks." - Yong Soek "I Passed my MLA exam Yesterday, thanks to this course! This course is excellent and highly comprehensive!" - Aditya "The course is brilliant! I have used around 2 weeks to review the course and have passed the exam in the first attempt!" - Martin "Great course, couldn't have passed the ML Associate exam without it! Covered everything needed to pass the exam about as succinctly as I could expect." - Nick - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Instructor My name is Stéphane Maarek, I am passionate about Cloud Computing, and I will be your instructor in this course. I teach about AWS certifications, focusing on helping my students improve their professional proficiencies in AWS. I have already taught 3,000,000+ students and gotten 800,000+ reviews throughout my career in designing and delivering these certifications and courses! With AWS becoming the centerpiece of today's modern IT architectures, I have decided it is time for students to learn how to be an AWS Machine Learning Engineer. So, let’s kick start the course! You are in good hands! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Instructor Hey, I'm Frank Kane, and I'm also co-instructing this course. I've successfully passed the MLA exam myself and have ensured everything you need to know is in here. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, and I'm best known for my top-selling courses in "big data", data analytics, machine learning, AI, Apache Spark, system design, and Elasticsearch. I hold 26 issued patents in the field of machine learning. I've been teaching on Udemy since 2015, where I've reached over one million students all around the world! I've worked hard to keep this course up to date with the latest developments in AWS machine learning, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - This course also comes with: Lifetime access to all future updates A responsive instructor in the Q&A Section Udemy Certificate of Completion Ready for Download A 30 Day "No Questions Asked" Money Back Guarantee! Join us in this course if you want to pass the AWS Certified Machine Learning Engineer Associate MLA-C02 / ME1-C02 exam and master the AWS platform!
- Prepare confidently for the AWS Certified Machine Learning Engineer Associate exam. (MLA-C02)
- Understand and apply key AWS machine learning and AI services like SageMaker, Bedrock, and more.
- Perform data preparation, feature engineering, and data validation for ML models.
- Master hyperparameter tuning, model training, and deployment strategies on AWS.
- Implement CI/CD pipelines and automation for scalable machine learning and AI workflows.
- Secure, monitor, and optimize AWS ML and AI infrastructure for performance and cost-efficiency.
Temario
UNIDAD 1 - Introduction · 6 leccionesUNIDAD 1 - Introduction
UNIDAD 1 - Introduction
- Introduction and Course Overview
- Optimizing your Udemy Learning Environment
- Get the Course Materials and Slides
- Heads Up! Choose the correct AWS Sign Up experience
- [Important] AWS Console UI Update
- Setting Up an AWS Billing Alarm
UNIDAD 2 - Data Ingestion and Storage · 49 leccionesUNIDAD 2 - Data Ingestion and Storage
UNIDAD 2 - Data Ingestion and Storage
- Intro: Data Ingestion and Storage
- Types of Data
- Properties of Data (The Three V's)
- Data Warehouses, Lakes, and Lakehouses
- Data Mesh
- ETL & ETL Pipelines and Orchestration
- Common Data Sources and Data Formats
- Amazon S3
- Amazon S3 - Hands On
- Amazon S3 Security - Bucket Policy
- Amazon S3 Security - Bucket Policy - Hands On
- Amazon S3 - Versioning
- Amazon S3 - Versioning - Hands On
- Amazon S3 - Replication
- Amazon S3 - Replication - Notes
- Amazon S3 - Replication - Hands On
- Amazon S3 - Storage Classes
- Amazon S3 - Storage Classes - Hands On
- Amazon S3 - Lifecycle Rules
- Amazon S3 - Lifecycle Rules - Hands On
- Amazon S3 - Event Notifications
- Amazon S3 - Event Notifications - Hands On
- Amazon S3 - Performance
- Amazon S3 - Encryption
- About DSSE-KMS
- Amazon S3 - Encryption - Hands On
- Amazon S3 - Default Encryption
- Amazon S3 - Access Points
- Amazon S3 - Object Lambda
- Amazon EBS
- Amazon EBS - Hands On
- Amazon EBS Elastic Volumes
- Amazon EFS
- Amazon EFS - Hands On
- Amazon EFS vs. Amazon EBS
- Amazon FSx
- Amazon FSx - Hands On
- Amazon Kinesis Data Streams
- Amazon Kinesis Data Streams - Hands On
- Amazon Data Firehose
- Kinesis Tuning and Troubleshooting
- Amazon Managed Service for Apache Flink
- Managed Service for Apache Flink - Hands On
- Kinesis Analytics Costs; RANDOM_CUT_FOREST
- Amazon MSK
- Amazon MSK - Connect
- Amazon MSK - Serverless
- Amazon Kinesis vs. Amazon MSK
- Quiz: Data Ingestion and Storage
UNIDAD 3 - Data Transformation, Integrity, and Feature Engineering · 39 leccionesUNIDAD 3 - Data Transformation, Integrity, and Feature Engineering
UNIDAD 3 - Data Transformation, Integrity, and Feature Engineering
- Intro: Data Transformation, Integrity, and Feature Engineering
- Elastic MapReduce (EMR) and Hadoop Overview
- EMR Serverless
- Apache Spark on EMR
- Feature Engineering and the Curse of Dimensionality
- Lab: Preparing Data for TF-IDF with Spark and EMR Studio, Part 1
- Lab: Preparing Data for TF-IDF with Spark and EMR Studio, Part 2
- Imputing Missing Data
- Dealing with Unbalanced Data
- Handling Outliers
- Binning, Transforming, Encoding, Scaling, and Shuffling
- Advanced Text Pre-Processing for AI
- Data Masking and Anonymization
- AI Training - Data Integrity
- UPDATE: SageMaker is now "SageMaker AI"
- SageMaker AI Overview
- SageMaker AI Domains
- Data Processing, Training, and Deployment with SageMaker AI
- Labeling and Annotating Training Data
- SageMaker Data Wrangler
- NOTICE: SageMaker Data Wrangler is getting hard to find
- Demo: SageMaker Studio, Canvas, and Data Wrangler
- Pre-Training Bias Metrics, and Mitigating Bias
- Partial Dependence Plots (PDPs), Shapley values, and SHAP
- SageMaker Feature Store
- SageMaker Canvas
- AWS Glue
- AWS Glue Studio
- AWS Glue Data Quality
- AWS Glue DataBrew
- Demo: Glue DataBrew
- Handling PII in DataBrew Transformations
- Intro to Amazon Athena
- Athena and Glue
- Athena and CREATE TABLE AS SELECT
- Athena Performance
- Athena ACID Transactions
- Athena Fine-Grained Access
- Quiz: Data Transformation, Integrity, and Feature Engineering
UNIDAD 4 - AWS Managed AI Services · 22 leccionesUNIDAD 4 - AWS Managed AI Services
UNIDAD 4 - AWS Managed AI Services
- Intro: AWS Managed AI Services
- Why AWS Managed Services?
- Amazon Comprehend
- Amazon Comprehend Custom Models
- Amazon Comprehend - Hands On
- Amazon Translate
- Amazon Translate - Hands On
- Amazon Transcribe
- Amazon Transcribe - Hands On
- Amazon Polly
- Amazon Polly - Hands On
- Amazon Rekognition
- Amazon Rekognition - Hands On
- Amazon Lex
- Amazon Lex - Hands On
- Amazon Personalize
- Amazon Textract
- Amazon Textract - Hands On
- Amazon Quick
- Amazon's Hardware for AI
- Amazon's Hardware for AI - Hands On
- Quiz: AWS Managed AI Services
UNIDAD 5 - SageMaker Built-In Algorithms · 22 leccionesUNIDAD 5 - SageMaker Built-In Algorithms
UNIDAD 5 - SageMaker Built-In Algorithms
- Intro: SageMaker Built-In Algorithms
- Introducing Amazon SageMaker
- SageMaker Input Modes
- Linear Learner in SageMaker
- XGBoost in SageMaker
- LightGBM in SageMaker
- Seq2Seq in SageMaker
- DeepAR in SageMaker
- BlazingText in SageMaker
- Object2Vec in SageMaker
- Object Detection in SageMaker
- Image Classification in SageMaker
- Semantic Segmentation in SageMaker
- Random Cut Forest in SageMaker
- Neural Topic Model in SageMaker
- Latent Dirichlet Allocation (LDA) in SageMaker
- K-Nearest-Neighbors (KNN) in SageMaker
- K-Means Clustering in SageMaker
- Principal Component Analysis (PCA) in SageMaker
- Factorization Machines in SageMaker
- IP Insights in SageMaker
- Quiz: SageMaker Built-In Algorithms
UNIDAD 6 - Model Training, Tuning, and Evaluation · 28 leccionesUNIDAD 6 - Model Training, Tuning, and Evaluation
UNIDAD 6 - Model Training, Tuning, and Evaluation
- Intro: Model Training, Tuning, and Evaluation
- Introduction to Deep Learning
- Deep Learning History and Overview
- Activation Functions
- Convolutional Neural Networks
- Recurrent Neural Networks
- Tuning Neural Networks
- Regularization Techniques for Neural Networks (Dropout, Early Stopping)
- L1 and L2 Regularization
- The Vanishing Gradient Problem
- The Confusion Matrix
- Precision, Recall, F1, AUC, and more
- RMSE, R-squared, MAE
- Ensemble Methods: Bagging and Boosting
- Automatic Model Tuning (AMT) in SageMaker
- Hyperparameter Tuning in AMT
- SageMaker Autopilot / AutoML
- SageMaker Studio, SageMaker Experiments
- Debugging Model Convergence and Training Performance
- SageMaker Model Registry
- MLFlow for LLM Experimentation
- Analyzing Training Jobs with TensorBoard
- SageMaker Training at Large Scale: Training Compiler, Warm Pools
- SageMaker Checkpointing, Cluster Health Checks, Automatic Restarts
- SageMaker Distributed Training Libraries and Distributed Data Parallelism
- SageMaker Model Parallelism Library
- Elastic Fabric Adapter (EFA) and MiCS
- Quiz: Model Tuning, Training, and Evaluation
UNIDAD 7 - Generative AI Model Fundamentals · 14 leccionesUNIDAD 7 - Generative AI Model Fundamentals
UNIDAD 7 - Generative AI Model Fundamentals
- Intro: Generative AI Model Fundamentals
- The Transformer Architecture
- Self-Attention and Attention-Based Neural Networks
- Applications of Transformers
- Generative Pre-Trained Transformers: How they Work, Part 1
- Generative Pre-Trained Transformers: How they Work, Part 2
- LLM Key Terms and Controls (tokens, embeddings, temperature, etc.)
- Fine-Tuning and Transfer Learning with Transformers
- Lab: Tokenization and Positional Encoding with SageMaker Notebooks
- Lab: Multi-Headed, Masked Self-Attention in SageMaker
- Lab: Using GPT within a SageMaker Notebook
- AWS Foundation Models and SageMaker JumpStart with Generative AI
- Lab: Using Amazon SageMaker JumpStart with Huggingface
- Quiz: Generative AI Model Fundamentals
UNIDAD 8 - Building Generative AI Applications with Bedrock · 47 leccionesUNIDAD 8 - Building Generative AI Applications with Bedrock
UNIDAD 8 - Building Generative AI Applications with Bedrock
- Intro: Building Generative AI Applications with Bedrock
- Building Generative AI with Amazon Bedrock and Foundation Models
- Bedrock Credentials: API Keys vs. IAM
- A note on Bedrock model access
- Lab: Chat, Text, and Image Foundation Models in the Bedrock Playground
- Fine-Tuning Custom Models and Continuous Pre-Training with Bedrock
- Low-Rank Adaptation (LoRA) - How Fine-Tuning Works
- Catastrophic Forgetting
- Model Distillation
- Retrieval-Augmented Generation (RAG) Fundamentals with Bedrock
- Vector Stores and Embeddings with Amazon Bedrock Knowledge Bases
- Optimizing your Vectors Store and Embeddings
- Mulitmodal Models and Pipelines with Bedrock
- Implementing RAG with Amazon Bedrock Knowledge Bases
- Lab: Building and Querying a RAG System with Amazon Bedrock Knowledge Bases
- Remember to delete your OpenSearch collection!
- Vector Stores for Bedrock: OpenSearch, S3 Vectors, Aurora pgvector
- S3 Vectors - Hands On
- Pre-Retrieval and Chunking Strategies
- Managing Chunking Strategies with Bedrock
- Re-Ranker Models in Amazon Bedrock
- Content Filtering with Amazon Bedrock Guardrails
- Lab: Building and Testing Guardrails with Amazon Bedrock
- Intro to Prompt Engineering
- Anatomy of a Prompt
- Prompt Best Practices
- Types of Prompts
- Prompt Management with Amazon Bedrock
- Basics of LLM Agents
- Multi-Agent Workflows
- Short and Long-Term Agent Memory
- Amazon AgentCore Introduction
- AgentCore Memory and Tools
- AgentCore Bedrock Import, Gateway, and Identity
- AgentCore Policies
- AgentCore Evaluators
- Lab: Amazon Bedrock AgentCore Harnesses
- Handling AgentCore Failure Modes
- Other Amazon Bedrock Features (Model Evaluation, Bedrock Studio, Watermarks)
- Model Context Protocol (MCP)
- Humans in the Loop
- Cost-Effective Model Selection
- Evaluation Techniques for Foundation Models
- Evaluating LLM's with ROUGE, BLUE, and BERT scores
- Amazon Bedrock Model Evaluations
- Deployment Validation Systems
- Quiz: Developing Generative AI Applications with Bedrock
UNIDAD 9 - Machine Learning and AI Operations (MLOps/ AIOps) with AWS · 44 leccionesUNIDAD 9 - Machine Learning and AI Operations (MLOps/ AIOps) with AWS
UNIDAD 9 - Machine Learning and AI Operations (MLOps/ AIOps) with AWS
- Intro: MLOps
- Optimizing Foundation Model Deployments
- Deployment Guardrails and Shadow Tests
- SageMaker's Inner Details and Production Variants
- SageMaker Resource Management: Instance Types and Spot Training
- SageMaker Resource Management: Automatic Scaling
- Token Efficiency
- Maximizing Resource Utilization and Throughput
- SageMaker: Deploying Models for Inference
- SageMaker Serverless Inference and Inference Recommender
- AI-Specific Cost Drivers
- SageMaker Inference Pipelines
- Model Performance Readiness and Drift Detection
- Monitoring ML Models in Production
- MLOps with SageMaker, Kubernetes, SageMaker Projects, and SageMaker Pipelines
- What is Docker?
- Amazon ECS
- Amazon ECS - Create Cluster - Hands On
- Amazon ECS - Create Service - Hands On
- Amazon ECR
- Amazon EKS
- Amazon EKS - Hands On
- AWS Batch
- AWS CloudFormation
- AWS CloudFormation - Hands On
- AWS CDK
- AWS CDK - Hands On
- AWS CodeDeploy
- AWS CodeBuild
- AWS CodePipeline
- AWS CodeCommit and CodeConnections
- CI/CD for AI Artifacts
- Automating RAG Updates
- Prompt / GenAI Regression Testing in CI/CD
- Git Review: Architecture and Commands
- Gitflow, GitHub Flow
- Amazon EventBridge
- Amazon EventBridge - Hands On
- AWS Step Functions
- AWS Step Functions: State Machines and States
- Amazon Managed Workflows for Apache Airflow (MWAA)
- AWS Lake Formation
- Lake Formation Data Filters
- Quiz: MLOps
UNIDAD 10 - Security, Identity, and Compliance · 25 leccionesUNIDAD 10 - Security, Identity, and Compliance
UNIDAD 10 - Security, Identity, and Compliance
- Intro: Security, Identity, and Compliance
- Principle of Least Privilege
- SageMaker Security: Encryption at Rest and in Transit
- SageMaker Security: VPC's, IAM, Logging and Monitoring
- IAM Introduction: Users, Groups, Policies
- IAM Users & Groups - Hands On
- AWS Console Simultaneous Sign-in
- IAM Policies
- IAM Policies - Hands On
- IAM MFA
- IAM MFA - Hands On
- IAM Roles
- IAM Roles - Hands On
- Encryption 101
- AWS KMS
- AWS KMS - Hands On
- Amazon Macie
- AWS Secrets Manager
- AWS Secrets Manager - Hands On
- VPC, Subnets, Internet Gateway, NAT Gateway
- NACL, Security Groups, VPC Flow Logs
- VPC Peering, Endpoints, VPN, Direct Connect
- VPC Cheat Sheet & Closing Comments
- AWS PrivateLink
- Quiz: Security, Identity, and Compliance
UNIDAD 11 - Management and Governance · 22 leccionesUNIDAD 11 - Management and Governance
UNIDAD 11 - Management and Governance
- Intro: Management and Governance
- Amazon CloudWatch - Metrics
- Amazon CloudWatch - Logs
- Amazon CloudWatch - Logs - Hands On
- Amazon CloudWatch - Logs Unified Agent
- Amazon CloudWatch - Alarms
- Amazon CloudWatch - Alarms - Hands On
- AWS X-Ray
- AWS X-Ray - Hands On
- NOTE: Amazon QuickSight is being rebranded as Amazon Quick Suite
- Overview of Amazon Quicksight
- Types of Visualizations, and When to Use Them
- Amazon CloudTrail
- Amazon CloudTrail - Hands On
- AWS Config
- AWS Config - Hands On
- CloudWatch vs. CloudTrail vs. Config
- AWS Budgets
- AWS Budgets - Hands On
- AWS Cost Explorer
- AWS Trusted Advisor
- Quiz: Management and Governance
UNIDAD 12 - Machine Learning Best Practices · 4 leccionesUNIDAD 12 - Machine Learning Best Practices
UNIDAD 12 - Machine Learning Best Practices
- Intro: Machine Learning Best Practices
- AWS Tools for Responsible AI
- AWS Well-Architected Machine Learning Lens
- Quiz: Machine Learning Best Practices
UNIDAD 13 - Practice Test · 1 leccionesUNIDAD 13 - Practice Test
UNIDAD 13 - Practice Test
- Full-length Practice Exam: MLA-C02 - AWS Certified Machine Learning Engineer Associate
UNIDAD 14 - Wrapping Up · 7 leccionesUNIDAD 14 - Wrapping Up
UNIDAD 14 - Wrapping Up
- Intro: Wrapping Up
- Additional Training Resources
- What to Expect
- Exam Walkthrough and Signup
- Save 50% on your AWS Exam Cost!
- Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers Only
- Bonus Lecture
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
14 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
12 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Frank Kane.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 14 unidades · 330 lecciones · 60 h
- 12 meses de acceso
- 12 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Frank Kane; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.


