The MLOps Engineering on AWS course provides comprehensive training in operationalizing machine learning (ML) workflows on the AWS platform. It equips learners with the skills and tools to automate, manage, and monitor ML models in production environments, following best practices of both DevOps and MLOps.

The course covers key areas such as:

• Introduction to MLOps and DevOps Integration: Understanding the principles and goals of MLOps, and how it extends DevOps practices to ML workflows.
• ML Lifecycle Management: Building, training, evaluating, deploying, and monitoring ML models in a structured and scalable manner.
• AWS Tools for MLOps: Leveraging services like Amazon SageMaker, SageMaker Model Monitor, and SageMaker Pipelines for streamlined ML operations.
• Automation and Orchestration: Using Apache Airflow and Kubernetes to orchestrate end-to-end ML workflows.
• Model Deployment Strategies: Implementing robust model deployment techniques including A/B testing, edge deployment, and rollback strategies.
• Monitoring and Governance: Incorporating monitoring by design, automated model checks, and ensuring model compliance and governance.
• Security in MLOps: Integrating security best practices into ML pipelines and safeguarding data and models.

By the end of this course, learners will be able to:

• Understand MLOps principles and the challenges of applying DevOps to machine learning workflows.
• Use AWS services and open-source tools to build scalable and maintainable ML pipelines.
• Deploy, monitor, and update ML models in production using best practices for automation and governance.
• Prepare for an AWS MLOps Certification, demonstrating hands-on expertise in MLOps on the AWS platform.

This course is ideal for professionals aiming to bridge the gap between data science and operations by mastering the full ML lifecycle on AWS.

Course Prerequisites

To get the most out of this course, learners should meet the following prerequisites:

• Basic knowledge of machine learning concepts and workflows
• Familiarity with cloud computing principles and AWS core services
• Understanding of DevOps concepts and CI/CD practices
• Proficiency in Python and scripting languages
• Experience using command-line interfaces
• Exposure to ML model training, evaluation, and deployment
• Working knowledge of containerization tools (e.g., Docker, Kubernetes)

These prerequisites ensure learners can fully engage with hands-on labs and practical exercises throughout the course.

Target Audience

The MLOps Engineering on AWS course is designed for professionals involved in the deployment, monitoring, and management of ML models, including:

• Data Scientists streamlining model deployment workflows
• DevOps Engineers transitioning into MLOps roles
• Machine Learning Engineers operationalizing ML systems
• IT Professionals managing ML workloads in the cloud
• Cloud Engineers specializing in AWS ML infrastructure
• Software Engineers expanding into the MLOps lifecycle
• AI/ML Product Managers overseeing model lifecycles
• Technical Project Managers managing ML infrastructure
• AWS Certified Professionals deepening their ML expertise
• System Administrators involved in ML model deployment and maintenance

Why Choose us

⭢ Live Online Training (Duration : 24 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

After completing the MLOps Engineering on AWS course, learners will be able to:

• Understand the goals and core principles of MLOps in the AWS ecosystem
• Distinguish MLOps from DevOps and identify unique challenges in ML workflows
• Build, train, evaluate, and deploy ML models using Amazon SageMaker
• Integrate security best practices into ML pipelines to ensure data and model safety
• Orchestrate ML workflows using Apache Airflow and Kubernetes
• Manage scalable deployment patterns such as batch, real-time, A/B testing, and edge deployment
• Monitor model performance and detect drift using SageMaker Model Monitor
• Create reusable pipelines for continuous integration and continuous delivery (CI/CD) of ML models
• Troubleshoot and maintain production ML systems efficiently
• Design an MLOps Action Plan to support operational excellence and automation