AWS Certified Machine Learning Engineer - Associate (MLA-C01)
Learn how to build production-ready ML systems. This course covers data engineering for ML, model training, tuning, deployment hosting on endpoints, SageMaker Pipelines, and MLOps strategies.
Who Should Join
ML engineers, data scientists, and developers wanting to deploy enterprise-grade AI models on AWS.
Prerequisites
Python coding knowledge and foundational understanding of machine learning algorithms.
Quizzes & Practice Exams
8+ Topic Quizzes & 2 Mock Exams
Curriculum & Exam Domains
Data Preparation and Feature Engineering using SageMaker Data Wrangler
Comprehensive coverage aligned with certification blueprints and practical implementation standards.
SageMaker Training Jobs, Hyperparameter Tuning, and Algorithms
Comprehensive coverage aligned with certification blueprints and practical implementation standards.
Model Deployments (Real-time, Batch, Serverless SageMaker Endpoints)
Comprehensive coverage aligned with certification blueprints and practical implementation standards.
SageMaker Pipelines and MLOps Best Practices
Comprehensive coverage aligned with certification blueprints and practical implementation standards.
Pre-trained AWS AI Services (Rekognition, Textract, Translate, Comprehend)
Comprehensive coverage aligned with certification blueprints and practical implementation standards.
Guided Hands-on Labs
Training a model using XGBoost on Amazon SageMaker
Deploying an inference endpoint with Auto Scaling enabled
Building a SageMaker ML pipeline to automate training and evaluation
Invoking pre-trained AI endpoints for text extraction and translation
Related AWS Certification Training Courses
Enquire About Our Training
Have questions about certifications, curriculum, or batch schedules? Get in touch with us today.