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Cbtnuggets - Aws Certified Machine Learning Engineer - Associate (mla-C01)
Released 12/2025
Taught by Scott Pletcher
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 179 Lessons ( 18h 30m ) | Size: 8.51 GB [/center]
This machine learning engineer course is designed to get you comfortable with the full lifecycle of machine learning workflows on AWS. You'll learn how to prepare data, train models, and deploy them securely in a professional environment. With AWS expert Scott Pletcher as your guide, you'll explore key AI and ML services like Amazon SageMaker and AWS Glue, and see how to apply them in your everyday work. You'll get hands-on with tools that simplify, accelerate, and enhance the way you use the AWS ecosystem. By the end, you'll be ready to earn a career-advancing credential from the world's cloud and AI powerhouse: the AWS Certified Machine Learning Engineer - Associate certification. Take the MLA-C01 exam with confidence and move forward in your machine learning career.
What you'll learn with AWS Machine Learning training
Build and train machine learning models with Amazon SageMaker
Prepare and clean datasets using AWS Glue
Automate model deployment with AWS Lambda
Apply security and compliance best practices for ML workloads
Optimize training performance and costs with Amazon EC2
Monitor and evaluate model accuracy using Amazon SageMaker AWS Machine Learning training FAQs
Who should take this AWS machine learning course?
If you're a DevOps or operations professional thinking about expanding into AI, this course is for you. You don't need deep AWS or data science experience, all it takes is some basic scripting and cloud familiarity. Developers, analysts, and DevOps engineers will all find something in this course to maximize their efficiency and improve their skills in data prep, model training, and deployment with AWS tools.
Is there an AWS machine learning certification I can take after this course?
Yes, this course prepares you for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam. That's a $150 AWS certification that's designed for anyone with about a year of experience using AWS services like SageMaker and AWS Glue. After this course, you'll be comfortable designing, building, and deploying machine learning models, as well as ready to certify as a ML engineer in production environments.
Does it pay well to learn how to use AWS AI services?
Yes. Companies are already paying for AWS AI services, and they're eager for machine learning engineers and DevOps pros who can extract value from AWS tools and get them a return on their investment. This machine learning engineer course gives you a mix of automation, data, and deployment expertise that often leads to salaries well above average in both IT operations and cloud development roles.
Does this course explain AWS SageMaker or AWS Glue?
Yes, this course explains all of AWS' AI services, AWS SageMaker and AWS Glue included. Take this machine learning engineer course and you'll learn to use SageMaker to build, train, and deploy models, and how to use AWS Glue for data preparation and transformation. You'll know enough to tackle the machine learning engineer certification, but you'll also know how to tackle real-world AI/ML problems in your job.
What jobs can I get with machine learning engineer training?
This AWS machine learning certification course readies you for roles like machine learning engineer, AI developer, cloud engineer, or DevOps engineer specializing in ML deployment. Really, any job that focuses on implementing and maintaining machine learning systems or bringing AI-powered solutions to real business challenges becomes much easier to do after this course.
Who is AWS Machine Learning training for?
This course is for DevOps engineers, system administrators, and cloud professionals looking to apply machine learning in AWS environments. It's a great fit for anyone moving from on-prem operations to cloud-based AI projects and looking for practical, production-ready ML experience.
Cbtnuggets - Aws Certified Machine Learning Engineer - Associate (mla-C01)
Released 12/2025
Taught by Scott Pletcher
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 179 Lessons ( 18h 30m ) | Size: 8.51 GB [/center]
This machine learning engineer course is designed to get you comfortable with the full lifecycle of machine learning workflows on AWS. You'll learn how to prepare data, train models, and deploy them securely in a professional environment. With AWS expert Scott Pletcher as your guide, you'll explore key AI and ML services like Amazon SageMaker and AWS Glue, and see how to apply them in your everyday work. You'll get hands-on with tools that simplify, accelerate, and enhance the way you use the AWS ecosystem. By the end, you'll be ready to earn a career-advancing credential from the world's cloud and AI powerhouse: the AWS Certified Machine Learning Engineer - Associate certification. Take the MLA-C01 exam with confidence and move forward in your machine learning career.
What you'll learn with AWS Machine Learning training
Build and train machine learning models with Amazon SageMaker
Prepare and clean datasets using AWS Glue
Automate model deployment with AWS Lambda
Apply security and compliance best practices for ML workloads
Optimize training performance and costs with Amazon EC2
Monitor and evaluate model accuracy using Amazon SageMaker AWS Machine Learning training FAQs
Who should take this AWS machine learning course?
If you're a DevOps or operations professional thinking about expanding into AI, this course is for you. You don't need deep AWS or data science experience, all it takes is some basic scripting and cloud familiarity. Developers, analysts, and DevOps engineers will all find something in this course to maximize their efficiency and improve their skills in data prep, model training, and deployment with AWS tools.
Is there an AWS machine learning certification I can take after this course?
Yes, this course prepares you for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam. That's a $150 AWS certification that's designed for anyone with about a year of experience using AWS services like SageMaker and AWS Glue. After this course, you'll be comfortable designing, building, and deploying machine learning models, as well as ready to certify as a ML engineer in production environments.
Does it pay well to learn how to use AWS AI services?
Yes. Companies are already paying for AWS AI services, and they're eager for machine learning engineers and DevOps pros who can extract value from AWS tools and get them a return on their investment. This machine learning engineer course gives you a mix of automation, data, and deployment expertise that often leads to salaries well above average in both IT operations and cloud development roles.
Does this course explain AWS SageMaker or AWS Glue?
Yes, this course explains all of AWS' AI services, AWS SageMaker and AWS Glue included. Take this machine learning engineer course and you'll learn to use SageMaker to build, train, and deploy models, and how to use AWS Glue for data preparation and transformation. You'll know enough to tackle the machine learning engineer certification, but you'll also know how to tackle real-world AI/ML problems in your job.
What jobs can I get with machine learning engineer training?
This AWS machine learning certification course readies you for roles like machine learning engineer, AI developer, cloud engineer, or DevOps engineer specializing in ML deployment. Really, any job that focuses on implementing and maintaining machine learning systems or bringing AI-powered solutions to real business challenges becomes much easier to do after this course.
Who is AWS Machine Learning training for?
This course is for DevOps engineers, system administrators, and cloud professionals looking to apply machine learning in AWS environments. It's a great fit for anyone moving from on-prem operations to cloud-based AI projects and looking for practical, production-ready ML experience.
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