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The Ultimate Roadmap: How I Cleared All AWS ML Certifications (With Resources & Tips)

The Ultimate Roadmap: How I Cleared All AWS ML Certifications (With Resources & Tips)

Are you looking to establish yourself in the AWS Machine Learning ecosystem but feeling overwhelmed about where to start? In this guide, I'll share my personal journey of successfully clearing all 3 AWS Machine Learning certifications

awscertifiedmachinelearningartificial-intelligencecloudcomputing
Aditya
Published Jan 10, 2025
As you can see from the image, my AWS ML certification journey took an unconventional path. I started with the most challenging certification first, then moved on to the others. Here's the order I followed:
  1. AWS Certified Machine Learning - Specialty
  2. AWS Certified AI - Practitioner (Beta)
  3. AWS Certified Machine Learning - Associate
My strategy was to tackle the toughest certification first, which made the subsequent certifications easier to approach. Let me break down my experience and tips for each certification.

1. AWS Certified Machine Learning - Specialty

This was the most challenging certification, but clearing it first set a strong foundation for the others. Focuses good amount of questions on ML concepts rather than AWS specifics.

Key Resources:

Notes:

  • Exploratory Data Analysis
  • Modelling
  • ML Implementation and operations
  • Amazon Sagemaker: Hands on
For more depth knowledge in sagemaker topics:

2. AWS Certified AI - Practitioner (Beta)

This exam focused on foundational AI and machine learning concepts, AWS AI services, and the responsible use of AI.

Key Resources:

Notes:

Some topics to revise:
  • Amazon SageMaker Autopilot
  • Amazon SageMaker Model Cards
  • Amazon SageMaker FAQs
  • Machine Learning Lens
  • Amazon Kendra
  • One Shot, Few Shot prompting

 3. AWS Certified Machine Learning - Associate

Last but not the least was the associate one. To round out my ML certifications, I completed the AWS Certified Machine Learning - Associate exam. This certification bridges the gap between the Practitioner and Specialty levels. This is a combination of Machine Learning - Specialty, AI Practitioner and Data Engineer Associate exams.


Key Resources:

Notes:

  • Use of EFS vs Lustre vs S3 for Sagemaker and Ml Data
  • Data Wrangler - Important
  • Kinesis
  • Revise data section of Udemy course
  • Revise XGBoost , Recall, Precision, Config, etc
  • Tensorboard on Sagemaker
  • DPL PDP Shapley graphs
  • Sagemaker domain execution role
  • Model Cards
  • Model monitor

Comprehensive list of AI services and keywords to revise:

  • Amazon SageMaker - Machine learning platform
  • Amazon Comprehend - Natural language processing
  • Amazon Rekognition - Image and video analysis
  • Amazon Polly - Text-to-speech service
  • Amazon Lex - Conversational interfaces and chatbots
  • Amazon Transcribe - Speech-to-text service
  • Amazon Translate - Language translation
  • Amazon Personalize - Real-time personalization and recommendation
  • Amazon Forecast - Time-series forecasting
  • Amazon Textract - Extract text and data from documents
  • Amazon Kendra - Enterprise search service
  • Amazon CodeGuru - Automated code reviews and application performance recommendations
  • Amazon Fraud Detector - Fraud detection service
  • Amazon Augmented AI (A2I) - Human review of ML predictions
  • Amazon DevOps Guru - ML-powered cloud operations service
  • Amazon Lookout for Vision - Computer vision for industrial quality inspection
  • Amazon Lookout for Equipment - ML-based equipment anomaly detection
  • Amazon Lookout for Metrics - Automated anomaly detection service
  • AWS DeepRacer - Autonomous racing car for reinforcement learning
  • AWS DeepLens - Deep learning-enabled video camera
  • AWS DeepComposer - AI-enabled musical keyboard
  • Amazon Connect - Cloud contact center with AI capabilities
  • Amazon Lex V2 - Conversational AI for chatbots (updated version)
  • Amazon QuickSight Q - Natural language query for business intelligence
  • Amazon SageMaker JumpStart - Machine learning model deployment
  • Amazon SageMaker Data Wrangler - Data preparation for ML
  • Amazon SageMaker Feature Store - Feature management for ML
  • Amazon SageMaker Clarify - Bias detection and explainability for ML models
It's important to remember that everyone's learning path is unique. While I chose to pursue these certifications in this particular order, your journey might look different. Some may prefer to start with the Associate level before moving to the Specialty, while others might focus solely on AI or ML. The key is to choose a path that aligns with your goals, experience, and learning style.
 

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