MLOps Engineering on AWS Course

The MLOps Engineering on AWS course is designed to provide participants with the professional depth of knowledge and hands-on experience needed to operationalize machine learning (ML) workflows using Amazon Web Services. The course walks through the entire MLOps lifecycle on AWS and covers the base principles of MLOps, how MLOPs is an extension of DevOps, builds CI/CD pipelines for ML systems, and the key processes of model building, model training, model evaluation, model deployment, and continuous monitoring and management of ML models on AWS-native tooling and services.

Participants will be guided through labs and real-world simulations to gain experience using a variety of AWS-native tools, including Amazon SageMaker, SageMaker Model Monitor, SageMaker Pipelines, as well as the use of Kubernetes to orchestrate training, model deployment, and Apache Airflow to automate workflows. In addition to incorporating best practices for model governance, version control, and security, participants will also gain experience deploying models in an automated way, A/B testing, and monitoring performance after deploying the models.

By the end of the course, learners will be well-prepared to pursue an AWS MLOps certification and effectively manage production-grade ML systems.  This course is for ML engineers, data scientists, and DevOps professionals to start a new chapter in their career. Delivered by SSDN Technologies, the Best IT Training Company, this training guarantees you receive top-quality instruction from the best of industry professionals. 


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Learning Options for You

  • Live Training (Duration : 24 Hours)
  • Per Participant

Fee: On Request

Course Prerequisites

  • Working knowledge of AWS services (S3, IAM, Lambda, SageMaker)
  • Familiarity with ML concepts and model training workflows
  • Experience with DevOps tools (CI/CD, Docker, Kubernetes) helpful
  • Programming experience with Python or related languages

Learning Objectives

The MLOps Engineering on AWS course equips learners to design, automate, and manage ML model lifecycles using AWS services. Participants gain hands-on experience with Amazon SageMaker, CI/CD pipelines, data versioning, automated deployment, model monitoring, and retraining workflows. The course emphasizes scalability, governance, reproducibility, and operational excellence in ML production environments. Completing this course prepares professionals to implement robust MLOps practices and accelerate machine learning delivery on AWS.

Target Audience

  • Machine learning engineers and data scientists
  • DevOps engineers supporting ML workflows
  • Cloud engineers deploying ML models at scale
  • IT professionals exploring MLOps on AWS for production environments

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