Machine Learning with SageMaker (Video Course)

Description

Master Cloud-Based Machine Learning on AWS

This course will teach critical skills to design and deploy scalable ML solutions. The core skills covered are:

• Data Preparation: Ingest, transform, and clean data using SageMaker
• Pipeline Automation: Build and deploy end-to-end ML workflows
• Solution Management: Monitor, secure, and optimize AWS-based ML systems

It offers hands-on AWS training that is ideal for software developers, data scientists, ML engineers, and DevOps professionals, this course blends theory with practical projects to help you design production-ready ML architectures, automate CI/CD pipelines for efficient deployment, implement security best practices for cloud-based systems.

You can find additional resources here: https://bitbucket.org/awsdevguru/awsmlassoc/src/main/

Related Learning:

• Sign up for more live trainings by Nick Garner: https://learning.oreilly.com/search/?q=nick%20garner&type=live-course&rows=100&language=en&publishers=Pearson

Skill Level:

• Intermediate to advanced

Course requirement:

• None

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Sample Content

Table of Contents

Module 1: Data Preparation for Machine Learning

Lesson 1: Data Ingestion and Storage Basics

Lesson 2: Data Transformation and Feature Engineering with SageMaker

Lesson 3: Preparing Data for Modeling

Module 2: ML Model Development

Lesson 4: Choosing and Training Models in SageMaker

Lesson 5: Model Evaluation and Bias Detection

Module 3: Deployment and Orchestration of ML Workflows

Lesson 6: Deploying Models with SageMaker

Lesson 7: Automating ML Workflows with SageMaker Pipelines

Module 4: ML Solution Monitoring, Maintenance, and Security

Lesson 8: Monitoring and Optimizing ML Solutions

Lesson 9: Securing ML Models and Data in SageMaker

Module 5: Exam Preparation and Strategy

Lesson 10: Exam Strategies and Practice

Updates

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