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Overview

Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate with ML. You will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.
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What You'll Learn

In this course, you will learn to:

  • Discuss the benefits of different types of machine learning for solving business problems
  • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
  • Explain how data scientists use AWS tools and ML to solve a common business problem
  • Summarize the steps a data scientist takes to prepare data
  • Summarize the steps a data scientist takes to train ML models
  • Summarize the steps a data scientist takes to evaluate and tune ML models
  • Summarize the steps to deploy a model to an endpoint and generate predictions
  • Describe the challenges for operationalizing ML models
  • Match AWS tools with their ML function

Who Should Attend

This course is intended for:

  • Development Operations (DevOps) engineers
  • Application developers
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Prerequisites

We recommend that attendees of this course have:

  • AWS Technical Essentials
  • Entry-level knowledge of Python programming
  • Entry-level knowledge of statistics

Learning Journey

Coming Soon...

Module 1: Introduction to Machine Learning

  • Benefits of machine learning (ML)
  • Types of ML approaches
  • Framing the business problem
  • Prediction quality
  • Processes, roles, and responsibilities for ML projects

Module 2: Preparing a Dataset

  • Data analysis and preparation
  • Data preparation tools
  • Demonstration: Review Amazon SageMaker Studio and Notebooks
  • Hands-On Lab: Data Preparation with SageMaker Data Wrangler

Module 3: Training a Model

  • Steps to train a model
  • Choose an algorithm
  • Train the model in Amazon SageMaker
  • Hands-On Lab: Training a Model with Amazon SageMaker
  • Amazon CodeWhisperer
  • Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks

Module 4: Evaluating and Tuning a Model

  • Model evaluation
  • Model tuning and hyperparameter optimization
  • Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker

Module 5: Deploying a Model

  • Model deployment
  • Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction

Module 6: Operational Challenges

  • Responsible ML
  • ML team and MLOps
  • Automation
  • Monitoring
  • Updating models (model testing and deployment)

Module 7: Other Model-Building Tools

  • Different tools for different skills and business needs
  • No-code ML with Amazon SageMaker Canvas
  • Demonstration: Overview of Amazon SageMaker Canvas
  • Amazon SageMaker Studio Lab
  • Demonstration: Overview of SageMaker Studio Lab
  • (Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint
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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

Course Curriculum

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Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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