Suleman S. Manji

Ai Learning Plan for Technology Consultants

 
  • Oct 14, 2023

Comprehensive Learning Plan: Mastering AWS AI in Healthcare with Operational Automation

Introduction

This guide is tailored for Technology Consultants with a focus on Process Automation, aiming to achieve mastery over AWS AI services in healthcare. It encompasses both technical and soft skills, real-world case studies, and operationalized automation for a holistic learning experience.

Week 1: Accelerated AWS Fundamentals

1. AWS Accelerated Course

  • Action: Choose an advanced AWS course focusing on AI/ML services.
  • Features: Basics of AWS, AI/ML services, cloud security.
  • Tips: Look for courses with hands-on labs for interactive learning.
  • Resources: AWS Well-Architected Framework
  • Time Management: Allocate 10-12 hours this week for this course.
  • Metrics: Successful completion of course assessments.

Week 2-3: Advanced Healthcare Data Management

1. Advanced Healthcare Data Management

  • Action: Take specialized courses on healthcare data management.
  • Features: Data types, EHR systems, HIPAA compliance.
  • Tips: Prioritize courses that offer real-world case studies.
  • Resources: HIPAA Compliance on AWS
  • Time Management: 8-10 hours per week.
  • Metrics: Ability to explain key healthcare data types and compliance requirements.

Week 4-6: AWS AI Services for Healthcare

1. AWS AI Specialized Training

  • Action: Opt for specialized AWS training that focuses on healthcare.
  • Features: AWS SageMaker, Comprehend Medical, Rekognition.
  • Tips: Use AWS’s own sandbox environments for hands-on practice.
  • Resources: AWS AI and ML Blogs
  • Time Management: 10-12 hours per week.
  • Metrics: Ability to create a basic healthcare-related model on SageMaker.

Week 7-9: SageMaker and Operational Automation

1. SageMaker for Automation

  • Action: Tutorials on automating ML workflows in healthcare using SageMaker.
  • Features: Data preprocessing, model training, model deployment.
  • Tips: Experiment with different ML models to compare performances.
  • Resources: SageMaker Python SDK
  • Time Management: 12-15 hours per week.
  • Metrics: Completion of at least two healthcare ML workflows.

Week 10-12: NLP and Medical Records with AWS Comprehend Medical

1. Comprehend Medical

  • Action: Use AWS Comprehend Medical for NLP solutions related to EHR.
  • Features: Named Entity Recognition, relationship extraction.
  • Tips: Look for open-source NLP projects in healthcare as inspiration.
  • Resources: AWS Comprehend Medical Documentation
  • Time Management: 10-12 hours per week.
  • Metrics: Successful extraction of key information from sample medical records.

Week 13-24: Consultancy-Driven Capstone Project

1. Capstone Project

  • Action: Identify a healthcare problem to solve using AWS AI services.
  • Features: Complete, client-ready project with extensive documentation.
  • Tips: Use Agile methodologies for project management.
  • Resources: AWS Project Planning Resources
  • Time Management: 15-20 hours per week.
  • Metrics: A portfolio-ready project and presentation.

Ongoing: Keeping Up-to-date and Networking

  • Action: Regularly read updates from AWS blogs, participate in webinars, and engage in online forums.
  • Metrics: Continuous learning and network growth.
  • Resources: AWS Online Tech Talks

Post-Completion: Next Steps

  • Action: Explore more advanced AWS certifications, consider contributing to open-source healthcare projects, or begin consultancy services focused on AWS and healthcare.
  • Metrics: Career advancement and contributions to the field.
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