The AWS Certified Machine Learning Engineer Associate certification covers data preparation, model development, deployment and monitoring of AI and ML on AWS, and this course follows the exam outline.
AWS Certified Machine Learning Engineer Associate Course in Australia
The AWS Certified Machine Learning Engineer Associate certification, exam code MLA-C02, is an Associate level AWS credential for ML engineering. AWS describes the exam as covering the building, deployment and maintenance of AI and ML solutions and pipelines on the AWS Cloud, for both traditional ML models and foundation models. This AWS machine learning engineer course follows the four content domains in the AWS exam guide.
Who this course suits
This AWS Certified Machine Learning Engineer Associate course suits AI engineers, backend software developers and data engineers. It also suits data scientists, DevOps developers and machine learning engineers.
What this course covers
The syllabus covers data preparation for ML and AI, ML model and foundation model development, deployment and orchestration of ML and AI workflows, and operating, monitoring and securing ML and AI solutions, with named AWS services such as Amazon SageMaker AI and Amazon Bedrock. Training will help prepare candidates for the AWS Certified Machine Learning Engineer Associate MLA-C02 exam.
Logitrain has been delivering high-quality, cost-effective training since 2004.
AWS Certified Machine Learning Engineer Associate is an Associate level certification in the AWS certification program. AWS organises its exam guide into four content domains, covering both traditional ML models and foundation models.
Logitrain provides digital course material, the price beat guarantee, and a choice of in-person, in-house or live online delivery. The exam is taken at any Pearson VUE test centre or online with Pearson VUE online proctoring.
AWS recommends at least 1 year of experience using Amazon SageMaker AI, Amazon Bedrock and other AWS services for ML engineering, and at least 1 year of experience in a related role such as backend software developer, DevOps developer, data engineer or data scientist. AWS does not make training mandatory for the exam.
Candidates new to AI on AWS can start with the AWS Certified AI Practitioner course.
Candidates can achieve this certification by passing the following exam.
MLA-C02 AWS Certified Machine Learning Engineer Associate
The exam is not included in the course fee. It is booked and paid separately through Pearson VUE.
Attempt it at any Pearson VUE test centre, or online through Pearson VUE online proctoring from a private space with a camera, microphone and speaker.
Exam format: a maximum of 65 multiple choice and multiple response questions, with a passing score of 720 on a scale of 100 to 1,000.
Digital course material included for the MLA-C02 AWS Certified Machine Learning Engineer Associate exam.
- Analysing and evaluating the performance of ML and AI systems
- Choosing appropriate modelling approaches for ML and AI solutions
- Collecting and storing data
- Implementing automated orchestration and CI/CD pipelines for MLOps and AI workloads
- Managing deployment infrastructure for ML and AI model types
- Monitoring ML and AI model inference and performance
- Optimising and managing ML and AI infrastructure costs and performance
- Performing data transformation, feature engineering and pre-processing
- Provisioning and configuring resources for ML and AI workloads
- Securing ML and AI workloads and model endpoints
- Training, fine-tuning and customising models for ML and AI solutions
- Validating data quality and managing bias
- AI engineers
- Backend software developers
- Data engineers
- Data scientists
- DevOps developers
- Machine learning engineers
- Data collection and storage
- Data transformation, feature engineering and pre-processing
- Data quality validation and bias management
- Amazon S3
- Amazon EBS
- Amazon EFS
- Amazon RDS
- Amazon DynamoDB
- Amazon OpenSearch Service
- AWS streaming data sources
- Amazon Kinesis
- Apache Flink
- Apache Kafka
- Apache Parquet
- JSON
- CSV
- ORC
- AWS Glue
- Apache Spark
- pgvector
- Amazon SageMaker Feature Store
- AWS Glue DataBrew
- Amazon EMR
- Amazon SageMaker Data Wrangler
- AWS Lambda
- Feature scaling
- Standardisation
- Feature splitting
- Binning
- Log transformation
- Normalisation
- Tokenisation
- Domain-specific augmentation
- Retrieval Augmented Generation (RAG)
- Chunking strategies
- Metadata extraction
- FM fine-tuning
- Continuous pre-training
- Model distillation
- AWS Glue Data Quality
- Dataset splitting
- Shuffling
- Augmentation
- Prompt-response pair validation
- Content safety screening
- Outlier detection
- Imputing missing data
- Deduplication
- Modelling approach selection for ML and AI solutions
- Model training, fine-tuning and customisation
- Performance analysis and evaluation of ML and AI systems
- Amazon Bedrock
- Foundation models
- Solution templates
- Interpretability
- Latency
- RAG architecture patterns
- Amazon Textract
- Amazon Rekognition
- Amazon Comprehend
- Amazon Transcribe
- Amazon SageMaker AI built-in algorithms
- SageMaker AI script mode
- Hyperparameter optimisation
- SageMaker AI automatic model tuning (AMT)
- Early stopping
- Distributed training
- Overfitting
- Underfitting
- Catastrophic forgetting
- Epoch
- Steps
- Batch size
- Task-specific prompt engineering
- Retrieval components
- Embedding models
- MLflow on SageMaker AI
- Amazon Bedrock evaluations
- Amazon Bedrock Prompt Management
- Model performance baselines
- Drift detection
- Shadow variants
- Production variants
- Human-in-the-loop workflows
- Bilingual evaluation understudy (BLEU)
- Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
- BERTScore
- Semantic similarity
- Model output assessment
- Bias detection
- LLM-as-a-judge frameworks
- RAG system monitoring
- Retrieval accuracy assessment
- Deployment infrastructure for ML and AI model types
- Resource provisioning and configuration for ML and AI workloads
- Automated orchestration and CI/CD pipelines for MLOps and AI workloads
- Multi-model deployment strategies
- Multi-container deployment strategies
- Real-time inference
- Batch processing
- Amazon Bedrock Custom Model Import
- Retrieval strategies
- Reranking
- On-demand and provisioned resources
- Containers
- SageMaker AI endpoints
- Amazon VPC
- SageMaker AI SDK for Python
- AWS CLI
- Boto3
- Auto scaling
- Amazon Bedrock knowledge bases
- Vector database configurations
- Document indexing
- Retrieval optimisation
- Retrieval pipelines
- GPU workloads
- Automated deployment strategies
- Rollback actions
- AWS CodeBuild
- AWS CodeCommit
- AWS CodeDeploy
- AWS CodePipeline
- AWS CodeConnections
- SageMaker Model Registry
- AI model testing frameworks
- Prompt testing
- Fine-tuned model versioning
- AI-specific pipeline orchestration
- Knowledge base refresh cycles
- Monitoring of ML and AI model inference and performance
- Optimisation and management of ML and AI infrastructure costs and performance
- Security for ML and AI workloads and model endpoints
- Amazon CloudWatch generative AI observability
- Amazon Bedrock Model Evaluation
- Drift detection pipelines
- A/B testing
- Coordination failure detection
- Truncated streaming
- Tool failures
- Inference instance families
- Amazon CloudWatch
- Amazon Bedrock AgentCore Observability
- AWS X-Ray
- Cost management tools
- Purchasing options
- FM inference costs
- Token usage optimisation
- Embedding computation costs
- Vector database storage optimisation
- Amazon CodeGuru
- Amazon Inspector
- Least privilege access
- IAM policies and roles
- AWS CloudTrail
- AWS Config
- VPCs
- Subnets
- Security groups
- Amazon Bedrock API keys
- IAM credentials
- Amazon Bedrock Guardrails
- Responsible AI policies
- AWS Certified AI Practitioner course: An AWS certification course in the AI area.
- The Machine Learning Pipeline on AWS course: A related course on machine learning pipelines on AWS.
- AWS Certified Machine Learning Specialty course: A related AWS machine learning certification course.
- AWS Data Analytics course: A related course on analytics on AWS.
- AWS courses and certifications: The AWS course category at Logitrain.
What is the AWS Certified Machine Learning Engineer Associate certification?
AWS Certified Machine Learning Engineer Associate is an Associate level AWS certification for ML engineering. AWS publishes the exam content outline that this course follows.
What does the AWS Certified Machine Learning Engineer Associate course cover?
The course covers the four content domains in the AWS exam guide: data preparation for ML and AI, ML model and foundation model development, deployment and orchestration of ML and AI workflows, and operating, monitoring and securing ML and AI solutions. Training will help prepare you for the AWS Certified Machine Learning Engineer Associate exam.
Is the exam included in the AWS Certified Machine Learning Engineer Associate course fee?
No. The official exam fee is not included in the course fee. The exam is booked and paid separately through Pearson VUE.
What are the prerequisites for the AWS Certified Machine Learning Engineer Associate course?
AWS recommends at least 1 year of experience using Amazon SageMaker AI, Amazon Bedrock and other AWS services for ML engineering, and at least 1 year of experience in a related role such as backend software developer, DevOps developer, data engineer or data scientist. AWS does not make training mandatory for the exam.
What is the current exam version for AWS Certified Machine Learning Engineer Associate?
The course is built around the AWS Certified Machine Learning Engineer Associate exam. AWS publishes current exam details on its certification page for this exam, so that page is the place to confirm the latest version.
How long is the AWS Certified Machine Learning Engineer Associate course?
The length depends on the schedule option chosen. Full-time weekdays, part-time weeknights and part-time weekends are available, and the current duration is shown in the course details on this page.
Where is the AWS Certified Machine Learning Engineer Associate training held?
Training is held in Melbourne, Sydney, Brisbane, Adelaide, Perth and Canberra, plus live online across Australia. Logitrain can also deliver the course in house at a client site.
Which course should come before the AWS Certified Machine Learning Engineer Associate course?
Individuals new to AI on AWS can start with the AWS Certified AI Practitioner course.
What is included with the MLA-C02 AWS Certified Machine Learning Engineer Associate Course?
Certification of Attendance
Get a Certificate of Attendance to prove your commitment to learning
Authorised Test Centre
Take the official certification exam at Logitrain, a local VUE test centre or online
Includes Course Material
Course material in digital format is included for flexibility and ease of use
Includes Mock & Test Questions
Mock test is included in the full-time courses to assist with your preparation
Highly Skilled Trainers
Our trainers are highly skilled with expertise and extensive hands-on experience
Our Price Beat Guarantee
Relax, we will beat competitor’s advertised price. Our course has no extra costs
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