Become an AWS Certified Machine Learning Engineer (MLA-C02)

A hands-on training program for the updated AWS Machine Learning Engineer – Associate exam. Prepare data, build and tune models in Amazon SageMaker, create generative AI apps with Amazon Bedrock, RAG and agents, and run it all in production with MLOps, security and monitoring.

350+ exam Q&A· 3 mock tests· 11 modules · 93 lessons· 35+ hands-on labs· 12 new GenAI & agentic AI topics· Updated for MLA-C02· 4 bonus beginner courses
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Your MLA-C02 learning path

From raw data to
AI in production.

The MLA-C02 exam checks whether you can build, deploy and maintain ML and generative AI solutions on AWS. The updated version adds foundation models, RAG and agentic AI to the traditional ML engineering skills.

Eleven modules follow the ML lifecycle — data, modeling, generative AI, MLOps, security and monitoring — with a hands-on lab set in almost every module. Topics marked New were added for MLA-C02.

Data
Module 1 — Data Preparation for ML and AI
Get data ready for ML and AI on AWS: S3 storage and access control, querying with Athena, cataloguing and ETL with Glue and Lake Formation, streaming with Kinesis, and vector databases for AI apps.
12 lessons · 6 labs
  • Object Storage Options
  • Simple Storage Service
  • S3 Bucket Policy
  • Amazon S3 ACL
  • S3 Object Versioning
  • Cross Region Replication
  • Amazon Athena
  • AWS Glue
  • AWS Lake Formation
  • Amazon Kinesis for ML
  • Vector Database for AI Applications
  • Multimodal Data Ingestion for AI Apps
  • ⚡ Lab — Create S3 Bucket, Upload and Access Files
  • ⚡ Lab — Introduction to AWS Glue
  • ⚡ Lab — AWS Glue & Athena: Analyze CSV Data in S3
  • ⚡ Lab — Running ETL Job using Glue
  • ⚡ Lab — Data Pipeline using Kinesis, Spark, and S3
  • ⚡ Lab — Store and Retrieve Vector Data for AI Applications
Data
Module 2 — Data Storage and Streaming for ML
Pick the right storage for ML workloads — EBS snapshots, EFS and FSx — and stream data in real time with Kinesis.
4 lessons · 1 lab
  • EBS Snapshots
  • Amazon Elastic File System
  • Amazon FSx
  • AWS Kinesis
  • ⚡ Lab — Amazon Kinesis Data Streams - Hands On
Data
Module 3 — Data Transformation & Integrity
Clean, transform and validate data: feature engineering, SageMaker Data Wrangler and Feature Store, data masking, and preparing documents for RAG and foundation model customization.
10 lessons · 7 labs · 3 new
  • Data Transformation
  • AWS Glue
  • Feature Engineering
  • SageMaker Data Wrangler
  • Data Quality & Validation
  • SageMaker Feature Store
  • Advanced Text Pre-processing New
  • Preparing Documents for RAG Applications New
  • Data Masking and Anonymization
  • Preparing Data for Foundation Model Customization
  • ⚡ Lab — Prepare Documents for RAG Pipeline New
  • ⚡ Lab — Building Data Pipelines with No-Code ETL
  • ⚡ Lab — Preparing Data for TF-IDF
  • ⚡ Lab — Data Preprocessing with DataBrew
  • ⚡ Lab — Data Prep and Automated Model Training
  • ⚡ Lab — Streamlining Data Analysis with SageMaker
  • ⚡ Lab — Generate Embeddings for Vector Search
Modeling
Module 4 — ML & Foundation Model Development
Build models with SageMaker Studio and built-in algorithms, choose and fine-tune foundation models in Amazon Bedrock, and pick the right RAG architecture.
7 lessons · 7 labs
  • Intro to SageMaker
  • Setting up Studio
  • SageMaker Features and Capabilities
  • SageMaker and Built-in Algorithms
  • Foundation Model Selection in Amazon Bedrock
  • Foundation Model Fine-Tuning Strategies
  • RAG Architecture Patterns
  • ⚡ Lab — Setting Up Jupyter Notebook
  • ⚡ Lab — Create & Manage SageMaker Studio
  • ⚡ Lab — Build a Sample Chatbot using Amazon Lex
  • ⚡ Lab — Build Lex Chatbot with Lambda & Third-Party API
  • ⚡ Lab — Build, Train, Deploy Model Using No-Code
  • ⚡ Lab — Compare Amazon Bedrock Foundation Models
  • ⚡ Lab — Select RAG Architecture by Use Case
Modeling
Module 5 — Model Training, Tuning, and Evaluation
Train, tune and evaluate: distributed training, hyperparameter tuning jobs, evaluation metrics for ML, NLP and generative AI, LLM-as-judge, and RAG evaluation.
13 lessons · 5 labs · 1 new
  • Model Training
  • Deep Dive into Model Training
  • Distributed Training
  • Hyperparameters for AWS
  • Tuning Job in AWS
  • Model Evaluation Metrics
  • LLM as Judge & Human Evaluation
  • AI Model Customization Techniques
  • Optimizing Retrieval and Embeddings
  • Human Evaluation Frameworks
  • NLP Evaluation Metrics
  • AI Evaluation Frameworks
  • RAG System Evaluation
  • ⚡ Lab — Hyperparameter Optimization using SageMaker
  • ⚡ Lab — Tune, Deploy, and Predict with TensorFlow
  • ⚡ Lab — Build, Train & Deploy ML Model with SageMaker AI
  • ⚡ Lab — Evaluate Generative AI Application Output
  • ⚡ Lab — RAG Retrieval Accuracy Evaluation New
Generative AI
Module 6 — Generative AI Model Fundamentals
How generative AI works under the hood — transformers, tokenization, embeddings and vector search — and when to fine-tune, use RAG or train a model.
6 lessons · 1 lab
  • Transformers
  • Transformers Advantages and Limitations
  • Tokenization
  • Embeddings & Vector Search
  • Fine-tuning vs RAG vs Training
  • Foundation Models
  • ⚡ Lab — Exploring Transformers Tokenization & GPT-2
Generative AI
Module 7 — AI Application Development & Deployment
Build and deploy AI applications with Amazon Bedrock: prompt engineering, RAG, agents, guardrails, and deployment options for foundation and external models.
10 lessons · 10 labs · 2 new
  • Introduction to Amazon Bedrock
  • Accessing Bedrock using Console
  • Prompt Engineering
  • RAG Concepts & Architecture
  • Agent Architecture
  • Guardrails
  • Foundation Model Deployment Options
  • Deploy External Models on AWS
  • Agent Deployment and Communication
  • RAG Configuration Optimization
  • ⚡ Lab — Invoke FMs to Generate Text & Image in the Console
  • ⚡ Lab — Invoke FM for Text Generation using API
  • ⚡ Lab — Mitigating Image Bias with Effective Prompts
  • ⚡ Lab — Set Up Guardrails with Amazon Bedrock
  • ⚡ Lab — Building a RAG using the Console
  • ⚡ Lab — Build a Bedrock Agent with Action Groups
  • ⚡ Lab — Protect Transcriptions via Amazon Transcribe
  • ⚡ Lab — Perform Sentiment Analysis with Amazon Comprehend
  • ⚡ Lab — Deploy Custom Model on AWS New
  • ⚡ Lab — Optimize Bedrock Agent Retrieval New
MLOps
Module 8 — ML & AI Workflow Orchestration and MLOps
Automate the ML lifecycle with Docker, EKS, CloudFormation, SageMaker Model Registry and MLflow, then scale knowledge bases, agentic workflows and AI CI/CD pipelines.
10 lessons · 5 labs · 3 new
  • AWS Containers & Docker
  • Amazon EKS
  • CloudFormation
  • SageMaker Model Registry & MLflow
  • Bedrock Knowledge Base Management
  • Retrieval Pipeline Implementation
  • Agent State Management
  • AI Workload Scaling
  • Agentic Workflow Infrastructure
  • AI CI/CD Pipeline
  • ⚡ Lab — Install Docker, Create Image & Push Image
  • ⚡ Lab — End-to-End MLOps on AWS SageMaker
  • ⚡ Lab — Build Bedrock Knowledge Base Pipeline New
  • ⚡ Lab — Deploy Agentic Workflow Infrastructure New
  • ⚡ Lab — Create AI CI/CD Pipeline New
Security
Module 9 — Security, Identity & Compliance for ML/AI
Secure ML and AI workloads with IAM, KMS and Secrets Manager, protect Bedrock apps with guardrails, and control access to foundation models.
7 lessons · 4 labs · 1 new
  • Identity and Access Management (IAM)
  • IAM Best Practices
  • AWS KMS
  • AWS Secrets Manager
  • Secure AI CI/CD Pipeline
  • Foundation Model Authentication
  • AI Safety and Data Protection
  • ⚡ Lab — Working with AWS IAM
  • ⚡ Lab — Enable Multi-Factor Authentication
  • ⚡ Lab — Secure Bedrock Apps with Guardrails
  • ⚡ Lab — Configure AI Access Controls New
Monitoring
Module 10 — Monitoring, Optimization & Governance
Monitor and govern models in production with CloudWatch, X-Ray, CloudTrail and Config, catch data and model drift, and keep ML and GenAI costs under control.
10 lessons · 5 labs · 2 new
  • What Are Monitoring Services
  • AWS X-Ray
  • CloudWatch Logs
  • AWS CloudTrail
  • AWS Config
  • Data Drift & Model Drift
  • Cost Optimization for ML
  • Agent Performance Monitoring
  • Foundation Model Monitoring
  • Foundation Model Cost Optimization
  • ⚡ Lab — Get Started with AWS X-Ray
  • ⚡ Lab — Enable CloudTrail and Store Logs in S3
  • ⚡ Lab — Setting Up AWS Config
  • ⚡ Lab — Monitor Bedrock Application Performance New
  • ⚡ Lab — Optimize GenAI Application Cost New
Best practices
Module 11 — ML & AI Best Practices
The ML lifecycle on AWS, responsible AI, and how to defend models against adversarial attacks.
4 lessons
  • Machine Learning
  • AWS Machine Learning Lifecycle
  • Responsible AI for ML
  • Adversarial Machine Learning
Exam prep
Exam Prep — Mock Tests & 350+ Exam Q&A
Three full mock tests and six exam Q&A sets — each set with answers to learn from, and without answers to test yourself — plus how to register for your exam.
3 mock tests · 6 Q&A sets · 350+ questions
  • ☑ Mock Test — Set 1
  • ☑ Mock Test — Set 2
  • ☑ Mock Test — Set 3
  • Exam Q&A Set 1 — with answers
  • Exam Q&A Set 1 — without answers (practice mode)
  • Exam Q&A Set 2 — with answers
  • Exam Q&A Set 2 — without answers (practice mode)
  • Exam Q&A Set 3 — with answers
  • Exam Q&A Set 3 — without answers (practice mode)
  • Exam Q&A Set 4 — with answers
  • Exam Q&A Set 4 — without answers (practice mode)
  • Exam Q&A Set 5 — with answers
  • Exam Q&A Set 5 — without answers (practice mode)
  • Exam Q&A Set 6 — with answers
  • Exam Q&A Set 6 — without answers (practice mode)
  • How to register for the AWS Certification exam
Certification track

Your path to passing MLA-C02

A clear order to follow, from your first lesson to exam day.

1
Cover the basics if you're new
Start with the beginner courses in cloud, AWS and AI so the core program makes sense from day one.
2
Work through the 11 modules
93 lessons across data preparation, model development, generative AI, MLOps, security and monitoring.
3
Build it yourself in 35+ labs
SageMaker, Bedrock, Glue, Kinesis, RAG pipelines, agents, guardrails and CI/CD — hands-on in AWS.
4
Test yourself with exam questions
Work through 350+ exam questions across 6 sets, then take the 3 mock tests to check you're ready.
5
Register and pass
Follow the exam-prep guide to book your AWS Certification exam.

Includes 350+ exam Q&A and 3 mock tests, plus 35+ hands-on labs.

See pricing →
Real transitions. Real offers.

They got the job.
Here's how.

Public reviews from K21 Academy learners who landed AI/ML roles real names, verified on Google and Trustpilot.

Trustpilot

K21 Academy: The Real Deal for Tech Leaders Transitioning into AI/ML and Cloud

As a VP of Software Engineering making a deliberate career transition into AI/ML and Cloud leadership, I've been extremely selective about where I invest my time and money. Enrolling in K21 Academy's AI/ML/GenAI/Agentic AI Track Program has been one of the best professional decisions I've made this year. Since joining, I've completed four courses Python, Mastery Gen AI, Agentic AI, and MLOps in AWS and successfully passed three Microsoft certification exams: AI-900, AZ-900, and AB-731. What sets K21 apart is the depth of knowledge their trainers bring. The hands-on projects on both Azure and AWS gave me practical experience that actually shows up on a resume and in interviews.

MO
Marcos Oliveira
VP of Software Engineering · California, USA
Google

A one-stop-shop for a great AI/ML career transition experience

K21 Academy is a great professional institution to meet your AI/ML career aspirations, and has the right training, courseware and job application, interview and placement support to help students achieve their goals. I particularly like the quality and depth of the faculty's knowledge, so conducive to learning. With AI/ML growing at such a rapid pace, it is imperative that we get connected with the right industry professionals who bring their experience and expertise to help students transition into a meaningful AI/ML career.

SS
Sanjeev Saksena
AI/ML Career Transition
Trustpilot

One of the best learning and professional growth experiences

I am 3 weeks into the AI/ML program. The support is extremely awesome for labs, doubts and even resume updates. Atul Sir is always available to address your concerns and provide career guidance. I am extremely impressed with all the live class teachers. The job and resume support is great. The team creates a custom game plan / learning path for everyone. The real world project experiences and hands-on lab experience is a key differentiator it sets this program apart from anything else I've tried.

GP
Gaj Paranjape
AI/ML Program · USA
Trustpilot

K21 Academy gave me clarity and confidence in my AI journey

I joined K21 Academy at a time when I felt stuck and unsure about my career direction. Connecting with Shahid was a turning point. He gave me clarity, practical guidance, and renewed confidence. One of K21 Academy's biggest strengths is its AI curriculum. The content is exhaustive and serves as a one-stop shop for AI and ML. I enrolled in the Full Stack AI program across multiple cloud platforms, and despite the significant time commitment, I haven't regretted it for a single moment.

GB
Gunita Bajaj
Full Stack AI Program · USA
Trustpilot

K21 Experience AI/ML Track gave me direction

I recently joined the AI/ML track course at K21 Academy and it has been a really helpful experience. When I first started learning AI and AWS, I was quite confused about where to begin and what direction to take. The structured curriculum, supportive mentors, and proper guidance have given me a lot of clarity. I also had a very positive experience working with Supriya at K21 Academy. She took the time to truly listen to my concerns and challenges, and I really appreciated her patience and attention to detail. I now feel more confident in my learning journey.

RS
Rahul Syal
AI/ML Learner · India
Google

From non-technical background to confident AI learner

I decided to explore AI as part of a career change, but coming from a non-technical background made me hesitant to enroll in any course. With the encouragement and support of my husband, I joined K21 Academy run by Atul Kumar and I'm really glad I did. The course is well-structured, catering to both beginners and advanced learners. What stands out the most is the thoughtful design of the program and the strong support system provided by the team. The emphasis on hands-on experience truly enhances the learning process.

MV
Monica Varry
AI Career Changer
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Bonus — beginner courses

New to cloud or AI?
Start here.

Four beginner courses that give you the foundations before you move on to the Machine Learning Engineer program.

For beginners
Cloud for Absolute Beginners
What cloud computing is, how it works, and why companies use it — no prior experience needed.
5 lessons · 4 labs
  • What is cloud computing and why it matters
  • IaaS, PaaS, SaaS explained simply
  • AWS, Azure, GCP — what each is good for
  • Core cloud concepts: regions, availability zones, scalability
  • Cloud pricing models and cost basics
  • Lab: Create an AWS Free Tier Account
  • Lab: Create an Azure Free Tier Account
  • Lab: Install Visual Studio Code, Python, Git on a Cloud VM
  • Lab: Install GitHub Copilot with VS Code
For beginners
AWS for Absolute Beginners
Core AWS services, pricing, free tier and billing setup — EC2, S3, RDS, IAM, VPC and more core concepts.
7 lessons · 2 labs
  • What is Cloud Computing and why AWS
  • EC2: instance types, AMIs, pricing models
  • S3: buckets, storage classes, lifecycle
  • RDS Managed Databases: MySQL, PostgreSQL, Aurora basics
  • IAM users, groups, roles, and permission policies
  • VPC Virtual Private Cloud, subnets, security groups
  • AWS Pricing Calculator & Cost Explorer overview
  • Lab: Create an AWS Free Tier Account
  • Lab: Set Up Billing Alerts & Budget Controls
AI
For beginners
AI/ML/GenAI for Beginners
The basics of artificial intelligence, machine learning and generative AI, explained simply.
5 lessons · 6 labs
  • What is AI, Machine Learning, and Deep Learning?
  • Generative AI and Large Language Models explained simply
  • Real-world AI use cases across industries
  • Introduction to Microsoft Foundry and Azure AI services
  • How models learn: training, inference, and evaluation
  • Lab: Create API Key for OpenAI GPT LLM Model
  • Lab: Create API Key for Anthropic (Claude) LLM Model
  • Lab: Get Started with Gen AI & Agents in Microsoft Foundry
  • Lab: Create API Keys for Tavily (search tool for RAG)
  • Lab: Create Weather API Key (tool for agents)
  • Lab: Create Serper API Key (web search tool)
AI
For beginners
AWS AI/ML/GenAI/Agentic AI for Beginners
Get hands-on with the AWS AI/ML stack — SageMaker, Bedrock and building APIs — plus the common labs and troubleshooting everyone needs.
5 lessons · 4 labs
  • Overview of the AWS AI/ML stack: SageMaker, Bedrock & AI services
  • Getting started with Amazon SageMaker Studio & Domains
  • Amazon Bedrock foundation models, playground & chat
  • Building APIs for AI/ML services with boto3, Lambda & API Gateway
  • Common setup & troubleshooting issues (IAM, quotas, regions)
  • Common Lab: Create a SageMaker Domain & Studio environment
  • Common Lab: Chat with foundation models in Amazon Bedrock
  • Common Lab: Build & deploy an inference API (boto3 + Lambda)
  • Common Lab: Troubleshooting AWS AI/ML IAM, quotas & regions
What the program includes

Everything to become an AWS ML engineer — and get certified.

Lessons, labs and exam preparation, updated for MLA-C02.

350+ Exam Q&A and 3 Mock Tests

Six exam Q&A sets, each with and without answers, three full mock tests, and a step-by-step guide to registering for your exam.

11 Modules, 93 Lessons

The full ML lifecycle on AWS: data preparation, storage and streaming, transformation, model development, training and evaluation, generative AI, MLOps, security, monitoring and best practices.

35+ Hands-On Labs

Work in Amazon SageMaker, Amazon Bedrock, AWS Glue, Kinesis, Lex, IAM, CloudTrail and more.

Generative AI & Agentic AI

Foundation models, RAG, Bedrock agents, knowledge bases, guardrails and AI CI/CD — the areas AWS added in MLA-C02.

4 Beginner Courses

Cloud for Absolute Beginners, AWS for Absolute Beginners, AI/ML/GenAI for Beginners, and AWS AI/ML/GenAI/Agentic AI for Beginners.

Choose your payment plan

Two plans. Pick the one that suits you.

The complete MLA-C02 program — 11 modules, 35+ hands-on labs, 350+ exam Q&A, 3 mock tests, generative AI and agentic AI coverage, and 4 bonus beginner courses.

Best Value
MLA-C02 Complete Program
Standard price $1,997
$997
Save $1,000 — one payment
  • AWS Machine Learning Engineer Associate [MLA-C02] course
  • 11 modules · 93 lessons
  • 35+ hands-on labs
  • Generative AI, RAG & agentic AI on AWS
  • 350+ exam Q&A across 6 sets
  • 3 full mock tests
  • Bonus: 4 beginner courses in cloud, AWS & AI
Enroll now — $997 →
Job outcomes

They got hired.
Here's what they said.

Every one of these learners came to K21 Academy with a goal. Every one of them shipped real projects, landed interviews, and got hired.

"

I'm thrilled to share that I've recently landed a role as a Generative AI Engineer. The hands-on projects let me confidently describe an end-to-end AI solution in the interview. The interviewer was genuinely impressed.

S
Steve
Generative AI Engineer
✓ Google review verified
"

I've successfully landed a job as an AI Engineer. Worked hands-on with cloud AI services and foundation models. Later received a second offer as a Generative AI Engineer. K21 gave me the skills and confidence to choose.

DD
Debasish Dash
AI Engineer → Generative AI Engineer
✓ Google review verified
"

I have finally secured a contract-to-hire role as a Generative AI Engineer. I was deploying a RAG pipeline to a web app during the actual hiring process. The projects I built at K21 were exactly what the company needed.

II
Ike Imala
Generative AI Engineer (contract-to-hire)
✓ Google review verified
"

Excited to share that I have successfully passed my AI certification exam! A big thank you to the K21 Academy team for their excellent training, well-structured course content, and continuous support. I highly recommend K21 Academy to anyone preparing for AI/ML certifications.

AA
Adnan Ahmed
AI/ML Engineer, Certified
✓ Trustpilot verified
Common questions

Before you enroll.

Who is this program for?
Machine learning engineers, data engineers, software developers, data scientists and anyone moving into MLOps or generative AI engineering on AWS who wants the AWS Certified Machine Learning Engineer – Associate certification.
What changed from MLA-C01 to MLA-C02?
AWS updated the exam to cover generative AI, agentic AI and foundation model/LLM workloads alongside traditional ML engineering. Registration for the MLA-C02 beta opened on September 1, 2026, and September 28, 2026 is the last day to take MLA-C01 in English. This program is built for MLA-C02, and new topics are marked in the curriculum.
Do I need prior experience?
AWS recommends at least one year of experience with AWS services for ML engineering. If you're newer than that, start with the four beginner courses in cloud, AWS and AI before the main program.
Is exam practice included?
Yes. You get 350+ exam questions across six Q&A sets — each set comes with answers to learn from and without answers to test yourself — plus three full mock tests.
Is the exam fee included?
No. You register and pay for the exam directly with AWS. The exam-prep lesson shows you how to register.
What score do I need to pass?
AWS reports results as a scaled score from 100 to 1,000, and the minimum passing score is 720.

Ready to become an AWS ML engineer?

Build, deploy and run ML and generative AI on AWS — and earn the MLA-C02 certification.

View pricing & enroll →
AWS Certified Machine Learning Engineer – Associate (MLA-C02) Training — by K21 Academy