TrainingArtificial intelligence / AWS Machine LearningAWS-EPMLA
AWS-EPMLAAWS · authorised trainingLeads to an exam
Exam Prep: AWS Certified Machine Learning Engineer – Associate (MLA-C01)
A one-day AWS course that prepares you for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam. The exam validates the ability to build, operationalise and maintain machine learning solutions and pipelines on the AWS Cloud. During the course you review the exam domains, work through exam-style questions, examine use cases and take part in group discussions. As a result, you assess your readiness, identify gaps in your knowledge and learn strategies for answering exam questions.
- Duration
- 1 day
- Level
- Intermediate
- Provider
- AWS
- Topic
- Artificial intelligence / AWS Machine Learning
Course outline
- Introduction
- Domain 1: data preparation for ML – ingestion and storage, transformation, feature engineering, data integrity
- Domain 2: ML model development – choosing a modelling approach, training and refining models, analysing performance
- Domain 3: deployment and orchestration of ML workflows – selecting and scripting infrastructure, CI/CD pipelines
- Domain 4: ML monitoring, maintenance and security – model inference, infrastructure costs, securing AWS resources
- Course completion
Skills you will gain
- Identify the scope and content tested by the MLA-C01 exam
- Practise exam-style questions and evaluate your preparation strategy
- Examine use cases and differentiate between them
- Identify gaps in your knowledge before taking the exam
- Recognise incorrect responses and apply strategies for tackling exam questions
Who should attend
- Individuals preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam
- Backend developers, DevOps engineers, data engineers and data scientists planning ML engineering certification on AWS
Prerequisites
- No specific training is required – the knowledge below is recommended before taking the MLA-C01 exam
- About one year of experience as a backend developer, DevOps developer, data engineer or data scientist
- Basic understanding of common ML algorithms and their use cases
- Data engineering fundamentals: data formats, ingestion, transformation and querying of data
- Software engineering best practices: modular, reusable code, deployment and debugging
- Experience with CI/CD pipelines (including AWS services for automating them), IaC and code repositories
- About one year of experience with Amazon SageMaker AI and other AWS services for building and deploying models
- Knowledge of AWS data storage and processing services and of deploying applications and infrastructure on AWS
- Familiarity with provisioning ML resources and with monitoring tools for logging and troubleshooting ML systems
- AWS security best practices: identity and access management, encryption, data protection
Certification
Exam
MLA-C01 - AWS Certified Machine Learning Engineer – Associate
This course prepares you for this exam.
2300 PLN
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