Machine Learning Engineering on AWS – A Comprehensive Course

In today’s AI-driven world, organizations are racing to adopt machine learning (ML) to transform data into actionable intelligence. However, building production-grade ML systems is not just about algorithms—it requires deep integration with cloud infrastructure, scalable pipelines, automation, and governance. This is where Machine Learning Engineering on AWS, a course delivered by CloudThat, becomes your launchpad into MLOps and real-world AI deployment.

Why Take This Course?

Most ML courses focus on model building—but Machine Learning Engineering is about much more:

• Automating end-to-end ML pipelines
• Integrating with AWS cloud-native services
• Ensuring scalability, reproducibility, and monitoring
• Deploying models for real-time or batch inference

What You’ll Learn

The Machine Learning Engineering on AWS course delivered by CloudThat equips you with the technical expertise to design, build, deploy, and monitor machine learning models using the rich toolset provided by AWS. Whether you’re aiming to build foundational knowledge or elevate your ML projects into production-ready workflows, this course ensures a comprehensive, hands-on learning experience.

Understand Machine Learning Foundations

Gain clarity on core ML concepts, use cases, and types of ML approaches. Learn how ML fits into business problem-solving and how AWS simplifies this process through Amazon SageMaker and other integrated services. You’ll also explore the importance of responsible AI and best practices for building ethical ML systems.

Data Preparation and Feature Engineering

Master the art of preparing and transforming raw data for machine learning. Learn how to:

• Handle missing, duplicate, and incorrect data
• Engineer and select features for optimal model performance
• Use services like Amazon SageMaker Data Wrangler, Amazon EMR, and SageMaker Processing to automate and scale data workflows

Model Selection, Training & Evaluation

Discover the wide range of built-in algorithms available in SageMaker and how to select the right one for your use case. Learn key concepts of:

• Automated model building with SageMaker Autopilot
• Training models efficiently using SageMaker Training Jobs
• Evaluating model performance and applying hyperparameter tuning techniques to optimize accuracy

Model Deployment and Inference

Explore strategies for deploying ML models into production. You’ll understand how to:

• Choose between real-time, batch, and asynchronous inference
• Optimize cost and performance based on inference instance types
• Apply traffic-shifting techniques such as A/B testing for smooth model updates

Implementing MLOps on AWS

Dive into the world of Machine Learning Operations (MLOps) using services like:

• Amazon SageMaker Pipelines for automated ML workflows
• Model Registry for tracking and managing model versions
• CI/CD integration to enable scalable, reproducible model deployment

Securing ML Workloads

Understand how to secure your machine learning environments by implementing:

• IAM-based access control
• Network isolation for model endpoints
• Secure storage and handling of sensitive data within pipelines

Monitoring and Maintenance of ML Models

Ensure ongoing performance with model monitoring tools like SageMaker Model Monitor. You’ll learn how to:

• Detect and respond to data drift and performance degradation
• Monitor model and data quality in production environments
• Implement automated alerts and remediation workflows

By the End of the Course, You Will Be Able To:

• Build end-to-end ML pipelines using AWS-native tools
• Choose the right ML approach and AWS services for your use case
• Deploy, secure, and monitor ML models in production
• Implement CI/CD and MLOps best practices in real-world scenarios

Hands-On Labs

Throughout the course, learners will build real-world pipelines including:

• Data Preprocessing using SageMaker Data Wrangler
• Model training and tuning on SageMaker
• Deployment of models
• Automating pipelines with SageMaker Pipeline
• Monitor data and model quality drift

Each module includes guided labs, quizzes, and mini-projects to reinforce learning by doing.

This course is ideal for data scientists, ML engineers, and cloud practitioners looking to bridge the gap between model development and production deployment using AWS.

Who Should Enroll?

This course is ideal for:

• Machine Learning Engineers transitioning to cloud-native MLOps
• Data Scientists who want to productionize their models
• DevOps Engineers entering the ML space
• Cloud Architects designing scalable AI systems

Prerequisites:

• Basic knowledge of Python and ML
• Familiarity with AWS core services (EC2, S3, IAM, etc.)

Why CloudThat?

CloudThat is an AWS Advanced Tier Training Partner and a recognized leader in upskilling professionals for cloud and AI careers. With expert instructors, hands-on labs, and real-world projects, CloudThat ensures:

• Certification readiness for AWS Machine Learning Engineer- Assciate
• Industry-aligned training curated by certified ML Engineers
• Access to a global learner community and mentoring

https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

Ready to Level Up Your ML Career?

Take your machine learning skills from experimentation to enterprise-grade production with Machine Learning Engineering on AWS by CloudThat. Whether you’re aiming to become an MLOps expert, streamline ML workflows, or simply deploy robust AI systems, this course gives you the tools to build, deploy, and manage ML at scale.

Enroll today and start your journey toward cloud-native ML engineering!

Visit CloudThat’s official website to explore the course and register.

https://www.cloudthat.com/training/aws/machine-learning-engineer-associate-on-aws

Get more information about AWS Machine Learning Engineer-Associate certification.

https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

About CloudThat