MLOps Engineering on AWS Course Overview

MLOps Engineering on AWS Course Overview

The MLOps Engineering on AWS course is designed to equip learners with the necessary skills to implement machine learning (ML) operations using the AWS platform. This comprehensive course covers the full spectrum of MLOps, including the principles and goals of MLOps, transitioning from DevOps to MLOps, and understanding the ML workflow within the context of MLOps. It also delves into development practices, such as building, training, and evaluating ML models, with a focus on security, integration with tools like Apache Airflow and Kubernetes, and leveraging Amazon SageMaker for streamlined operations.

Aspiring participants can also gain hands-on experience through various labs and demonstrations that include deploying models to production, conducting A/B testing, and monitoring ML models with tools like Amazon SageMaker Model Monitor. Upon completion, learners will have a solid foundation to prepare for an AWS MLOps Certification, demonstrating their proficiency in MLOps engineering on AWS, and the ability to apply best practices for operationalizing machine learning systems.

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  • Live Online Training (Duration : 24 Hours)
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  • Including Official Coursebook
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  • Live Online Training (Duration : 24 Hours)
  • Per Participant
  • Including Official Coursebook

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

To successfully undertake the MLOps Engineering on AWS course, students are expected to meet the following minimum prerequisites:

  • Basic understanding of machine learning concepts and terminology.
  • Familiarity with cloud computing principles, particularly the AWS ecosystem.
  • Experience with DevOps practices and tools.
  • Knowledge of programming and scripting languages such as Python.
  • Comfort with command-line interfaces and development environments.
  • Prior exposure to machine learning model building, training, and evaluation processes.
  • Understanding of containerization technologies, ideally Docker and Kubernetes.

These prerequisites are designed to ensure that participants can fully engage with the course content and participate effectively in hands-on labs. With this foundation, students will be well-prepared to learn and apply MLOps practices on AWS.

Target Audience for MLOps Engineering on AWS

The MLOps Engineering on AWS course equips learners with the skills to integrate ML workflows with DevOps practices on AWS.

  • Data Scientists seeking to streamline ML workflows
  • DevOps Engineers transitioning into MLOps roles
  • Machine Learning Engineers interested in operationalizing ML models
  • IT Professionals aiming for expertise in deploying and monitoring ML models on AWS
  • Cloud Engineers looking to specialize in ML infrastructure on AWS
  • Software Engineers wanting to understand the MLOps lifecycle
  • AI/ML Product Managers overseeing the end-to-end ML model lifecycle
  • Technical Project Managers looking to manage MLOps projects
  • AWS Certified professionals aiming to deepen their MLOps knowledge
  • System Administrators interested in ML model deployment and management

Learning Objectives - What you will Learn in this MLOps Engineering on AWS?

Introduction to the Course's Learning Outcomes:

This MLOps Engineering on AWS course equips students with the skills to automate and streamline ML workflows, ensuring efficient model operations and deployment on AWS.

Learning Objectives and Outcomes:

  • Understand the concept of Machine Learning Operations (MLOps) and its goals in automating ML workflows.
  • Learn the transition from traditional DevOps to MLOps and the unique considerations in ML workflows.
  • Gain hands-on experience with AWS services to build, train, and evaluate machine learning models within MLOps pipelines.
  • Acquire knowledge on integrating security best practices into MLOps processes.
  • Familiarize with Apache Airflow and Kubernetes for orchestrating and scaling ML workflows.
  • Master the use of Amazon SageMaker's suite of tools to streamline the MLOps lifecycle, including model training, tuning, and deployment.
  • Develop skills to package models, manage inference operations, and deploy models to production with robustness and scalability.
  • Conduct A/B testing and deploy models to edge devices, understanding various deployment patterns.
  • Implement monitoring solutions for ML models using Amazon SageMaker Model Monitor and learn the importance of monitoring by design.
  • Create an MLOps Action Plan and troubleshoot common issues in MLOps pipelines, ensuring continuous improvement and operational excellence.