Udemy – AWS Certified Machine Learning Engineer Associate – Complete 2025-1

Udemy – AWS Certified Machine Learning Engineer Associate – Complete 2025-1 Downloadly IRSpace

Aug 16, 2025 - 07:19 Updated: Aug 17, 2025 - 19:56

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Udemy – AWS Certified Machine Learning Engineer Associate – Complete 2025-1

The AWS Certified Machine Learning Engineer Associate – Complete course is designed for those who want to master machine learning on AWS and earn the certification. With a perfect balance of theory and practical topics, this course covers all the services required for the MLA-C01 exam. Each section includes conceptual lectures followed by practical demonstrations, where learners implement what they have learned in real AWS environments. This course goes beyond just preparing for the AWS Certified Machine Learning Engineer Associate exam. Learners develop practical skills in designing, implementing, and maintaining machine learning solutions on AWS that they can apply to real-world projects.

Some of the highlights of this course include: Complete coverage of all AWS services required for certification, including Amazon SageMaker, Bedrock, Comprehend, Rekognition, and many more. Hands-on learning by following hands-on demos for each key service, reinforcing theoretical concepts with real-world implementation experience. Master data preparation by developing expertise in AWS data analytics services such as Glue, EMR, Athena, and Kinesis to prepare data for machine learning workflows. Learn how to deploy and orchestrate machine learning models using various AWS services and build automated CI/CD pipelines for machine learning workflows. Also, implement best practices for monitoring machine learning solutions, optimizing costs, and securing your machine learning infrastructure using IAM, KMS, and other security services.

What you will learn

Learners will master key AWS machine learning services including SageMaker, Bedrock, and Comprehend through hands-on demonstrations and practical implementations.
They learn how to design, deploy, and monitor machine learning pipelines on AWS using services such as CloudWatch, CloudTrail, and SageMaker Model Monitor.
Implementing data preparation workflows using AWS data analytics services including Glue, EMR, and Athena for effective development of machine learning models is another skill acquired in this course.
Learners will develop secure and cost-effective machine learning solutions by implementing best practices in IAM policies, encryption, and resource optimization.

This course is suitable for people who:

This course is ideal for IT professionals, software developers, data engineers, and cloud professionals preparing for the AWS Certified Machine Learning Engineer Associate certification exam. This course is designed for those looking to expand their machine learning skills on AWS, whether they are currently working with AWS services or are planning to transition into machine learning roles. The comprehensive curriculum of this course is useful for both certification candidates and professionals looking to implement practical machine learning solutions using AWS services. With theoretical lectures and hands-on demonstrations, this course is valuable for learners at all stages of the machine learning journey, from those with basic AWS knowledge to experienced developers looking to specialize in AWS machine learning implementations.

AWS Certified Machine Learning Engineer Associate – Complete Course Details

Publisher: Udemy
Instructor: Deepak Dubey
Training level: Beginner to advanced
Training duration: 33 hours and 23 minutes
Number of lessons: 119

Course syllabus in 2025/5

AWS Certified Machine Learning Engineer Associate – Complete Course Prerequisites

Basic cloud computing knowledge is helpful but not required. This course is designed for IT professionals with some programming experience in Python, Java, or similar languages. Familiarity with data concepts and AWS fundamentals is beneficial, but comprehensive hands-on demonstrations will guide you through each service. You’ll need an AWS account (free tier is sufficient) and a computer with internet access to follow along with the practical exercises.