[100% Off] Aws Certified Machine Learning Engineer Practice Test 2025
Hands-on guide to Amazon SageMaker, MLOps, Deep Learning, and AI Services like Rekognition. Pass the MLS-C01 exam!
What you’ll learn
- Build
- train
- tune
- and deploy machine learning models at scale using the full capabilities of Amazon SageMaker.
- Integrate pre-trained AI services like Rekognition (vision) and Comprehend (NLP) into your applications using APIs.
- Prepare and process massive datasets for ML workloads using core AWS data services like S3
- Glue
- and Athena.
- Design and automate end-to-end MLOps workflows using SageMaker Pipelines and AWS Step Functions for CI/CD.
Requirements
- A basic understanding of Python programming and fundamental machine learning concepts (e.g.
- supervised learning
- model training) is required. Familiarity with core AWS services (S3
- EC2) is beneficial but not mandatory. You will need an AWS account to perform the hands-on labs.
Description
Machine Learning is transforming our world, and Amazon Web Services (AWS) is the number one platform where this revolution is happening. To build a future-proof career in technology, mastering the AWS Machine Learning stack is no longer just an advantage—it’s a necessity. This course, fully updated for August 2025, is the only resource you need to go from a beginner to a confident AWS ML practitioner.This is not just a theoretical overview. It is a comprehensive, hands-on journey designed to give you the practical skills you’ll use on the job and need to pass the AWS Certified Machine Learning – Specialty (MLS-C01) exam.
Who is this course for?
-
Data Scientists who want to break free from local machine limitations and scale their models in the cloud.
-
Software Developers aiming to build the next generation of intelligent, AI-powered applications.
-
Solutions Architects who need to design robust, scalable, and cost-effective ML infrastructure.
-
Aspiring ML Engineers looking for a structured path to mastering the most in-demand cloud skills.
What will you learn by doing? We will cover the entire AWS Machine Learning ecosystem in depth, with a focus on practical application:
-
Master Amazon SageMaker: Go from A-to-Z with the flagship AWS ML service. You will perform data labeling, build processing jobs, train models with built-in algorithms and your own custom code, perform hyperparameter tuning, and deploy production-ready endpoints.
-
Implement MLOps: Learn the critical skill of automating ML workflows. We will build robust CI/CD pipelines for your models using SageMaker Pipelines and AWS Step Functions.
-
Leverage AI Services: Go beyond model building and learn to integrate powerful, pre-trained AI services like Amazon Rekognition (image/video analysis), Comprehend (NLP), and Transcribe (speech-to-text) directly into your applications.
-
Build Data Pipelines for ML: Understand how to properly ingest, store, and process massive datasets using S3, AWS Glue, and Athena.
