Exam Prep MLA-C01: AWS Machine Learning Engineer Associate

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The Exam Prep MLA-C01: AWS Certified Machine Learning Engineer – Associate specialization is designed for professionals seeking to build and deploy scalable, production-grade machine learning solutions using Amazon Web Services (AWS). This comprehensive learning path empowers learners with practical, hands-on experience in developing, optimizing, and operationalizing ML workflows using services like Amazon SageMaker, AWS Glue, Amazon S3, AWS AI/ML APIs, and more. Each course builds technical proficiency and exam readiness for the AWS Certified Machine Learning – Associate (MLA-C01) certification. AWS: Machine Learning & MLOps Foundations AWS: Feature Engineering, Data Transformation & Integrity AWS: Model Training, Optimization & Deployment AWS: ML Workflows with SageMaker, Storage & Security AWS: Managed AI Services Each course is divided into Modules, Lessons, and Video Lectures, delivering a blend of 4 to 4.5 hours of hands-on and theoretical content. Learners can assess their progress with Practice and Graded Assignments after each module, reinforcing core concepts and real-world readiness. By completing this specialization, learners will be able to: Design, implement, and automate scalable machine learning workflows on AWS Prepare and transform data for ML using AWS Glue, DataBrew, and Feature Store Train, tune, evaluate, and deploy models using Amazon SageMaker

Syllabus

Course 1: AWS: Machine Learning & MLOps Foundations
Course 2: AWS: Feature Engineering Data Transformation & Integrity
Course 3: AWS: Model Training , Optimization & Deployment
Course 4: AWS: ML Workflows with SageMaker, Storage & Security
Course 5: AWS: Managed AI Services

Courses

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10 hours 22 minutes
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AWS: ML Workflows with SageMaker, Storage & Security is the fourth course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course enables learners to design secure, scalable, and efficient machine learning workflows on AWS, focusing on key pillars: data storage, model development, and security. Learners will begin by exploring how to collect, store, and stream ML data using services like Amazon S3, Amazon Kinesis, and Amazon Redshift. The course then transitions into hands-on model development with Amazon SageMaker, including data preparation, training, and deployment processes. In the final module, learners are introduced to the critical aspects of security and data protection, learning how to secure ML pipelines using IAM, KMS, encryption, and network controls. This course prepares learners to build production-grade ML systems that not only scale efficiently but also meet enterprise-level compliance and security requirements. This course consists of three comprehensive modules, each divided into focused lessons and practical demonstrations. Learners will gain approximately 3–3.5 hours of video content, featuring step-by-step tutorials using AWS services and real-world ML pipeline examples. Graded and Ungraded Quizzes are included in every module to test knowledge and practical readiness. Module 1: Data Storage & Real-Time Streaming on AWS Module 2: Data Preparation & ML Model Development with Amazon SageMaker Module 3: Security, Identity & Data Protection on AWS By the end of this course, learners will be able to: Design end-to-end ML workflows using AWS storage, compute, and ML services Process streaming and batch data sources for ML model development Secure ML pipelines using IAM, encryption, and network controls Build compliance-ready ML solutions using Amazon SageMaker and supporting services This course is ideal for cloud developers, ML engineers, and data professionals with hands-on experience in AWS who are looking to master the integration of machine learning workflows with enterprise-grade data management and security. It is especially valuable for those preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam, with a focus on storage, model development, and secure deployment practices.
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5 hours 51 minutes
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AWS: Feature Engineering, Data Transformation & Integrity is the second course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course enables learners to build essential skills in preparing and transforming data for machine learning workloads using AWS services. It provides a structured, hands-on understanding of data cleaning, feature engineering, encoding techniques, and scalable ETL workflows on AWS. Learners will start by mastering data preparation techniques, including cleaning, transformation, and feature extraction. The course explores methods to improve model accuracy by engineering meaningful features and applying categorical encoding strategies such as One-Hot Encoding, Label Encoding, and Tokenization. Learners will also understand the importance of maintaining data integrity and fairness, addressing bias, and securely handling sensitive information (PII) using tools like AWS Glue DataBrew. In the second module, learners will gain practical experience with AWS-native tools for scalable data engineering. This includes working with AWS Glue for ETL job orchestration, Glue Data Quality for dataset validation, and AWS Glue DataBrew for code-free data profiling and transformation. Learners will also dive into Amazon EMR, processing large-scale datasets using Apache Spark to build powerful, distributed data pipelines tailored for ML workflows. The course is divided into two modules, each broken down into lessons and practical video walkthroughs. Learners can expect approximately 2.5 to 3 hours of video lectures, combining theoretical knowledge with hands-on guidance using AWS ML services. Each module also includes Graded and Ungraded Quizzes to reinforce understanding and assess readiness. Module 1: Data Preparation & Transformation Techniques Module 2: ETL & Data Engineering with AWS Glue and EMR By the end of this course, learners will be able to: - Clean, transform, and engineer data effectively for ML use cases - Apply categorical encoding techniques for machine learning models - Ensure fairness, integrity, and compliance in dataset preparation - Use AWS Glue, Glue DataBrew, and EMR for scalable, production-ready data pipelines This course is ideal for machine learning practitioners, data engineers, and developers with 6 months to 1 year of AWS experience. It is also valuable for learners preparing for the MLA-C01 exam who want to deepen their hands-on skills in data transformation, feature engineering, and large-scale ETL on AWS.
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5 hours 48 minutes
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"AWS: Fundamentals of Machine Learning & MLOps is the first course of Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course assists learners in building foundational knowledge of core machine learning concepts, including types of learning, data preparation, model evaluation, and operationalization. Learners gain a strong understanding of the difference between AI, Deep Learning, and Machine Learning, and how to identify and apply real-world ML use cases using AWS services. This course allows learners to explore key topics such as model selection, classification workflows, confusion matrices, and regression evaluation techniques. In addition, learners are introduced to the concepts of MLOps and the AWS services used to streamline ML deployment and monitoring in production environments. The course is divided into two modules, and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:30–3:00 hours of video lectures that provide both theory and hands-on knowledge using AWS tools. Also, Graded and Ungraded Quizzes are provided with every module to test the understanding and application readiness of learners." Module 1: Machine Learning and MLOps Concepts Module 2 : Model Development & Evaluation Techniques By the end of this course, learners will be able to: - Apply foundational machine learning and MLOps concepts using AWS tools - Build and evaluate ML models with services like Amazon SageMaker - Understand end-to-end ML workflows, including data preparation, model training, and deployment - Strengthen their preparation for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam This course is ideal for aspiring ML practitioners, data engineers, and developers with 6 months to 1 year of AWS experience who want to build practical skills in machine learning and MLOps. It also supports learners preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam and professionals seeking hands-on knowledge of implementing and managing ML workflows using AWS services.
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5 hours 25 minutes
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AWS: Managed AI Services is the fifth course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course is designed to help learners leverage powerful pre-trained AI services offered by AWS to accelerate the development of intelligent applications — without the need to build or train ML models from scratch. Through a practical, service-based approach, learners will explore AWS offerings for natural language understanding, speech processing, computer vision, personalized recommendations, intelligent search, and human-in-the-loop workflows. These services allow developers, data engineers, and ML practitioners to embed intelligence into applications at scale with minimal ML expertise. With real-world examples, this course demonstrates how to use services like Amazon Comprehend, Rekognition, Polly, Textract, Transcribe, Personalize, Kendra, and A2I (Augmented AI) to deliver business value through managed AI capabilities. This course is structured into two major modules, each containing focused lessons and guided walkthroughs. Learners will gain approximately 2.5 to 3 hours of hands-on video content, supported by Graded and Ungraded Quizzes to assess conceptual understanding and real-world application. Module 1: Natural Language, Speech & Vision AI Services on AWS Module 2: Intelligent Search, Personalization & Human-in-the-Loop AI By the end of this course, learners will be able to: Understand how AWS Managed AI Services solve common AI use cases without custom model development Integrate services for NLP, speech, and computer vision into applications Build personalized experiences and intelligent search solutions using Amazon Personalize and Kendra Incorporate human review using Amazon A2I for critical workflows requiring oversight This course is ideal for cloud developers, solution architects, data engineers, and ML beginners who want to integrate powerful AI capabilities without training models. It’s also tailored for learners preparing for the MLA-C01 certification, especially those aiming to master the application of AWS’s fully managed AI services in real-world use cases.
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8 hours 52 minutes
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AWS: Model Training, Optimization & Deployment is the third course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course is designed to equip learners with the skills to train, optimize, and deploy machine learning models efficiently using AWS services. Learners begin by exploring popular algorithms such as Linear Learner, XGBoost, LightGBM, and k-Nearest Neighbors (k-NN), and understand their use cases in classification and regression tasks.You’ll then dive into the model training process, learning how to configure key parameters like epochs, batch size, and steps for optimized performance. Then the learners will begin by exploring SageMaker Model Debugger and SageMaker Experiments, which help monitor training jobs and compare experiment results efficiently.You’ll then dive into cross-validation techniques and learn how to apply hyperparameter tuning using both random search and Bayesian optimization methods to improve model accuracy. Finally by exploring compute options such as Amazon ECS, Amazon EKS, and AWS Lambda, followed by infrastructure management with AWS CloudFormation.You’ll learn how to implement auto scaling policies for ML workloads and choose the right SageMaker compute instance types (CPU vs. GPU) for different deployment scenarios. This course is divided into three comprehensive modules, each containing targeted lessons and practical demonstrations. Learners will benefit from approximately 3.5 to 4 hours of expert-led video content, featuring real-world use cases and hands-on walkthroughs using AWS tools. Every module includes Graded and Ungraded Quizzes to assess conceptual understanding and application. Module 1: Model Training, Algorithms & Inference Techniques Module 2: Model Optimization, Evaluation & Tuning with SageMaker Module 3: Scalable Infrastructure & Automated ML Deployment on AWS By the end of this course, learners will be able to: Compare real-time and batch inference approaches to determine the best strategy for model deployment. Apply model optimization techniques such as hyperparameter tuning Understand and select appropriate inference strategies for deployment Explore AWS compute and orchestration services like ECS, EKS, Lambda, and CloudFormation for ML deployment. This course is ideal for ML practitioners, data scientists, and cloud developers who are looking to scale their ML workflows and gain hands-on experience with advanced features of Amazon SageMaker. It is also designed for learners preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam, focusing on the model training and deployment aspects of the certification.