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As artificial intelligence and machine learning (AI/ML) are quickly becoming part of our day-to-day, it is becoming increasingly more important to understand how to collaborate efficiently with data scientists and build applications that integrate with ML. The Practical Science with Amazon SageMaker course will help you in your developer or DevOps engineer role understand the basics of ML and the steps involved in building ML models using Amazon SageMaker Studio. In this one-day, classroom training course an expert AWS instructor will walk you through how to prepare data and train, evaluate, tune, and deploy ML models.

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What You'll Learn

  • Learn how to define machine learning (ML) and deep learning
  • Learn how to identify the concepts in a deep learning ecosystem
  • Use Amazon SageMaker and the MXNet programming framework for deep learning workloads
  • Fit AWS solutions for deep learning deployments

Who Should Attend

This course is intended for:

  • Developers who are responsible for developing deep-learning applications
  • Developers who want to understand the concepts behind deep learning and how to implement a deep learning solution on AWS Cloud
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Prerequisites

We recommend that attendees of this course have:

  • A basic understanding of ML processes
  • Knowledge of AWS core services like Amazon EC2 and AWS SDK
  • Knowledge of a scripting language like Python

Learning Journey

Want to boost your career in AWS? Click on the roles below to see the learning pathways, specially designed to give you the skills to succeed.

  • Cloud DevOps Engineer
  • Cloud Operations Engineer
  • Cloud Operations Manager
  • Cloud Security Architect
  • DevOps Administrator
  • Kubernetes Administrator
  • Lead Cloud Software Developer
  • Security Administrator
  • Security Engineer

Module 1: Machine learning overview

  • A brief history of AI, ML, and DL
  • The business importance of ML
  • Common challenges in ML
  • Different types of ML problems and tasks
  • AI on AWS

Module 2: Introduction to deep learning

  • Introduction to DL
  • The DL concepts
  • A summary of how to train DL models on AWS
  • Introduction to Amazon Sage Maker
  • Hands-on lab: Spinning up an Amazon Sage Maker notebook instance and running a multilayer perceptron neural network model

Module 3: Introduction to Apache MX Net

  • The motivation for and benefits of using MX Net and Gluon
  • Important terms and APIs used in MX Net
  • Convolutional neural networks (CNN) architecture
  • Hands-on lab: Training a CNN on a CIFAR-10 dataset

Module 4: ML and DL architectures on AWS

  • AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk)
  • Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Recognition)
  • Hands-on lab: Deploying a trained model for prediction on AWS Lambda
This course is not associated with any Certification.

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 16 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.

Keep Exploring

Course Curriculum

Course Curriculum

Training Schedule

Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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