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Overview

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.
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What You'll Learn

In this course, you will learn to:

  • Explain the benefits of MLOps
  • Compare and contrast DevOps and MLOps
  • Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
  • Set up experimentation environments for MLOps with Amazon SageMaker
  • Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)
  • Describe three options for creating a full CI/CD pipeline in an ML context
  • Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code)
  • Demonstrate how to monitor ML based solutions
  • Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top of newly acquired data

Who Should Attend

This course is intended for:

  • MLOps engineers who want to productionize and monitor ML models in the AWS cloud
  • DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production
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Prerequisites

We recommend that attendees of this course have:

  • AWS Technical Essentials course
  • DevOps Engineering on AWS course, or equivalent experience
  • Practical Data Science with an Amazon SageMaker course, or equivalent experience

Learning Journey

Coming Soon...

Day 1

Module 1: Introduction to MLOps

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

Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio

  • 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

Module 3: Repeatable MLOps: Repositories

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

Module 4: Repeatable MLOps: Orchestration

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

Day 2

Module 4: Repeatable MLOps: Orchestration (continued)

  • 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

Module 5: Reliable MLOps: Scaling and Testing

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

Day 3

Module 5: Reliable MLOps: Scaling and Testing (continued)

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

Module 6: Reliable MLOps: Monitoring

  • 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

Frequently Asked Questions (FAQs)

  • Why get AWS certified?

    AWS certifications validate your expertise in cloud computing and your proficiency in using AWS services.

    These certifications are globally recognized and highly sought after by employers, as they demonstrate your ability to design, deploy, and manage scalable and secure cloud solutions on the AWS platform.

    AWS-certified professionals are in high demand, opening doors to new career opportunities and higher earning potential.

  • What to expect for the examination?

    AWS offers a variety of certification exams at different levels (Foundational, Associate, Professional, and Specialty) covering various domains and services.

    The exams typically consist of multiple-choice and multiple-response questions, and some may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is AWS certification valid for?

    AWS certifications are valid for three years from the date of passing the exam.

    To maintain your certification, you will need to recertify by passing the latest version of the exam or completing the AWS Cloud Quest game-based training (if option is applicable).

    Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 24 countries (and counting!).

    - Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.

    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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Course Curriculum

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Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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