Course Overview:

The MLOps Engineering on AWS course is designed to provide hands-on experience and knowledge in building, training, deploying, monitoring, and managing machine learning models on AWS. The course will guide you through setting up the environment, designing ML pipelines, and implementing the best practices to ensure high-performing and scalable solutions.

Introduction to MLOps on AWS: Understanding the key concepts and principles of MLOps and its importance in the machine learning lifecycle. Exploring the AWS ecosystem for MLOps, including Amazon SageMaker, AWS Lambda, Amazon S3, AWS Batch, and more.

Building and Deploying Machine Learning Models on AWS: Designing and implementing end-to-end machine learning pipelines on AWS. This includes data preprocessing, model training, model evaluation, and model deployment using Amazon SageMaker and other AWS services.

Monitoring and Managing Machine Learning Models on AWS: Monitoring machine learning models in production using Amazon CloudWatch and other AWS monitoring tools. Understanding best practices for managing and scaling machine learning infrastructure on AWS.

Optimizing and Scaling Machine Learning Workloads on AWS: Troubleshooting and optimizing machine learning pipelines for performance and scalability. Exploring strategies for automating and scaling machine learning workloads using AWS Batch, AWS Lambda, and other AWS services.

After completing this course, students will be able to:

  • Apply MLOps principles and tools to manage the machine learning lifecycle.
  • Leverage AWS services for MLOps, such as SageMaker, Lambda, and S3.
  • Design and implement end-to-end machine learning pipelines.
  • Deploy, monitor, and manage machine learning models on AWS.
  • Troubleshoot and optimize machine learning pipelines for performance.

Upcoming Batches

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Key Features:

  • Comprehensive Curriculum: The course covers all aspects of MLOps on AWS, from basic concepts to advanced topics like scaling and optimization.
  • Hands-On Labs: Participants will have the opportunity to work on real-world scenarios and projects, gaining practical experience with AWS tools and services.
  • Experienced Instructors: The course is taught by industry experts with years of experience in machine learning and AWS, ensuring high-quality instruction.
  • Flexible Schedule: The course is offered in both online and in-person formats, with options for full-time or part-time study.
  • Certification: Participants will receive a certificate of completion at the end of the course, showcasing their expertise in MLOps on AWS.

Who Should Attend:

  • Data scientists and machine learning engineers
  • Software developers
  • IT professionals
  • Technical leads and managers
  • Professionals seeking career advancement

Prerequisites:

  • AWS Technical Essentials course (classroom or digital)
  • DevOps Engineering on AWS course, or equivalent experience
  • Practical Data Science with Amazon SageMaker course, or equivalent experience.
  • Learning objective of the course:

    • Understand the principles of MLOps and its significance in the machine learning lifecycle.
    • Develop machine learning pipelines on AWS using Amazon SageMaker, AWS Lambda, Amazon S3, AWS Batch, and AWS Step Functions.
    • Deploy, manage, and monitor machine learning models on AWS.
    • Optimize and scale machine learning workloads using AWS services.
    • Troubleshoot and optimize machine learning pipelines for performance and scalability.
    • Apply best practices for managing and scaling machine learning infrastructure on AWS.
    • Gain hands-on experience with real-world scenarios and projects.

    Course Outline: Download Course Outline

    • Processes
    • People
    • Technology
    • Security and governance
    • MLOps maturity model

    • Bringing MLOps to experimentation
    • Setting up the ML experimentation environment
    • Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
    • Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
    • Workbook: Initial MLOps

    • Managing data for MLOps
    • Version control of ML models
    • Code repositories in ML

    • ML pipelines
    • Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines

    • End-to-end orchestration with AWS Step Functions
    • Hands-On Lab: Automating a Workflow with Step Functions
    • End-to-end orchestration with SageMaker Projects
    • Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects
    • Using third-party tools for repeatability
    • Demonstration: Exploring Human-in-the-Loop During Inference
    • Governance and security
    • Demonstration: Exploring Security Best Practices for SageMaker
    • Workbook: Repeatable MLOps

    • Scaling and multi-account strategies
    • Testing and traffic-shifting
    • Demonstration: Using SageMaker Inference Recommender
    • Hands-On Lab: Testing Model Variants

    • Hands-On Lab: Shifting Traffic
    • Workbook: Multi-account strategies

    • The importance of monitoring in ML
    • Hands-On Lab: Monitoring a Model for Data Drift
    • Operations considerations for model monitoring
    • Remediating problems identified by monitoring ML solutions
    • Workbook: Reliable MLOps
    • Hands-On Lab: Building and Troubleshooting an ML Pipeline

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    Course ID: 19946

    Course Price at

    $1599 + 0% TAX
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