AWS and Google AI Certifications: Cloud ML Credentials Compared

AWS and Google Cloud offer competing AI certification paths, each reflecting their platform's strengths and philosophy. Choosing between them—or pursuing both—depends on your organization's cloud strategy, existing skills, and career goals.

This guide compares AWS and Google AI certifications, helping you navigate these important cloud ML credentials.

The Cloud AI Certification Landscape

Three major cloud providers dominate AI certification:

• Microsoft Azure: Most comprehensive portfolio, strong Microsoft 365 Copilot integration (see Part 7)
• AWS: Deepest technical credentials, largest cloud market share
• Google Cloud: Strong ML focus, TensorFlow integration, AI research heritage

This guide focuses on AWS and Google certifications. Most organizations will prioritize certifications matching their primary cloud platform.

AWS AI Certification Portfolio

AWS Certified AI Practitioner (AIf-C01)

Launched in late 2024, this foundational certification covers AI basics on AWS.

What it covers:

• Fundamentals of AI and ML
• AWS AI and ML services overview
• Responsible AI practices
• AI use case identification
• Basic implementation concepts

Exam format:

• 85 questions (multiple choice and multiple response)
• 120 minutes
• $150 USD
• Passing score: 700/1000

Prerequisites:

• None required
• Recommended: 6 months general AWS experience
• Basic understanding of AI/ML concepts helpful

Preparation time:

• No AI background: 40-60 hours
• Some AI knowledge: 25-35 hours
• AWS experience: 20-30 hours

Key AWS services covered:

• Amazon SageMaker basics
• Amazon Bedrock (generative AI)
• Amazon Q (AI assistant)
• Amazon CodeWhisperer
• AWS AI services (Rekognition, Comprehend, Textract, Transcribe, Polly)

Study resources:

• AWS Skill Builder learning paths (free)
• AWS AI Practitioner exam guide
• Hands-on labs with AWS AI services
• Practice exams

Who should get this:

• Business professionals needing AWS AI literacy
• Cloud practitioners exploring AI
• Project managers on AI initiatives
• Sales and marketing professionals in tech
• Foundation for AWS ML Specialty

Career impact:

• Entry-level AWS AI credential
• Good foundation for advanced certifications
• Demonstrates AWS AI familiarity
• Limited standalone career impact

AWS Certified Machine Learning - Specialty (MLS-C01)

The premier AWS ML credential, highly respected and technically demanding.

What it covers:

Data engineering (20%):

• Creating data repositories for ML
• Identifying and implementing data ingestion solutions
• Identifying and implementing data transformation solutions

Exploratory data analysis (24%):

• Sanitizing and preparing data
• Performing feature engineering
• Analyzing and visualizing data for ML

Modeling (36%):

• Framing business problems as ML problems
• Selecting appropriate models
• Training and evaluating ML models
• Performing hyperparameter optimization

ML implementation and operations (20%):

• Building ML solutions for performance, availability, scalability, resiliency, and fault tolerance
• Recommending and implementing appropriate ML services and features
• Applying security practices to ML solutions
• Deploying and operationalizing ML solutions

Exam format:

• 65 questions (multiple choice and multiple response)
• 180 minutes
• $300 USD
• Passing score: 750/1000

Prerequisites:

• 1-2 years hands-on experience with AWS ML services (recommended)
• Strong understanding of ML algorithms and practices
• Experience with data engineering and modeling
• Python proficiency (or other programming language)
• AWS cloud experience

Preparation time:

• With prerequisites: 80-120 hours
• Without ML background: 150-200+ hours
• Requires substantial hands-on practice

Key AWS services covered:

• Amazon SageMaker (comprehensive)
• AWS Glue for data preparation
• Amazon EMR for big data processing
• Amazon S3 for data storage
• AWS Lambda for ML inference
• Amazon ECR and ECS for containers
• AWS security services for ML

Study resources:

• AWS Skill Builder ML learning plan
• A Cloud Guru / Pluralsight courses
• Official AWS ML Specialty study guide
• Hands-on labs and projects (essential)
• Practice exams (Tutorials Dojo, Whizlabs)
• AWS ML blog and documentation

Hands-on practice areas:

• Build end-to-end ML pipelines in SageMaker
• Implement various ML algorithms
• Deploy models to production
• Monitor and optimize model performance
• Implement ML security and compliance

Who should get this:

• ML engineers working on AWS
• Data scientists deploying to AWS
• AI solution architects
• Data engineers supporting ML workloads
• Senior developers building ML systems

Not ideal for:

• ML beginners (learn ML fundamentals first)
• Non-technical roles
• Those not using AWS
• Professionals without programming experience

Career impact:

• Highly valued credential
• Average salary: $120,000-170,000+
• Qualifies for senior ML engineer roles
• Strong demand in enterprise AWS environments
• Commands premium in cloud ML market

Google Cloud AI Certification Portfolio

Google Cloud Professional Machine Learning Engineer

Google's flagship ML certification, emphasizing production ML systems.

What it covers:

Architecting low-code ML solutions (12%):

• Building ML models using BigQuery ML
• Using pre-built ML APIs
• Building applications using AutoML

Collaborating within and across teams (16%):

• Managing ML team workflows
• Translating business requirements to ML specifications
• Exploring and preparing data
• Ensuring responsible AI practices

Scaling prototypes into ML models (18%):

• Building and training models
• Selecting and optimizing ML infrastructure
• Tracking and auditing model artifacts

Serving and scaling models (19%):

• Serving models for online and batch predictions
• Automating model retraining
• Testing and validating model performance
• Monitoring model performance

Automating and orchestrating ML pipelines (21%):

• Designing and implementing ML pipelines
• Implementing CI/CD for ML systems
• Automating model versioning and tracking

Monitoring, optimizing, and maintaining ML solutions (14%):

• Identifying and mitigating training/serving bias
• Managing model versions
• Troubleshooting ML solutions
• Tuning ML solution performance

Exam format:

• 50-60 questions (multiple choice and multiple select)
• 2 hours
• $200 USD
• Passing score: Scaled score, not publicly disclosed

Prerequisites:

• 3+ years industry experience (recommended)
• 1+ years designing/managing GCP ML solutions (recommended)
• Proficiency in Python
• Strong ML fundamentals
• Google Cloud platform knowledge

Preparation time:

• With prerequisites: 80-100 hours
• Without GCP experience: 120-150 hours
• Substantial hands-on practice required

Key Google Cloud services covered:

• Vertex AI (comprehensive ML platform)
• BigQuery ML
• AutoML and AI Platform
• TensorFlow on Google Cloud
• Dataflow for data processing
• Cloud Storage and Bigtable
• Kubernetes Engine for ML
• AI Platform Pipelines (Kubeflow)

Study resources:

• Google Cloud Skills Boost learning path
• Coursera specialization (Machine Learning with TensorFlow on GCP)
• Official Google exam guide
• Hands-on labs via Qwiklabs
• Practice exams and sample questions
• Google Cloud documentation

Hands-on practice areas:

• Build ML pipelines with Vertex AI
• Deploy models using various serving options
• Implement MLOps practices
• Use BigQuery ML for SQL-based ML
• Create AutoML models
• Manage model lifecycle and versioning

Who should get this:

• ML engineers on Google Cloud
• Data scientists deploying production ML
• ML platform engineers
• AI solution architects using GCP
• MLOps practitioners

Not ideal for:

• ML beginners
• Non-technical roles
• Those not using Google Cloud
• Professionals seeking foundational certification (consider Cloud Digital Leader instead)

Career impact:

• Respected technical credential
• Average salary: $115,000-165,000+
• Qualifies for ML engineer roles on GCP
• Growing demand as GCP adoption increases
• Strong signal of production ML capability

Google Cloud Professional Data Engineer

While not exclusively AI-focused, this certification is relevant for ML practitioners.

ML-relevant coverage:

• Data pipeline design for ML
• BigQuery for ML data preparation
• Building streaming data systems
• Data governance and quality for ML

When to consider:

• You work at intersection of data engineering and ML
• Your role emphasizes data infrastructure for ML
• You want to demonstrate broader data platform expertise
• Your organization uses GCP extensively for data

AWS vs. Google AI Certifications: Direct Comparison

Foundational Level

AWS AI Practitioner vs. Google Cloud Digital Leader (with AI focus):

AWS AI Practitioner:

• More AI-specific
• Deeper coverage of AI services
• Better for AI-focused roles
• Newer (less established)

Google Cloud Digital Leader:

• Broader cloud focus
• AI is one component
• Better for general cloud literacy
• More established credential

Verdict: AWS AI Practitioner for AI-specific foundation; Google Cloud Digital Leader for broader cloud understanding.

Advanced ML Engineering

AWS ML Specialty vs. Google Professional ML Engineer:

Similarities:

• Both highly technical and demanding
• Require hands-on ML experience
• Cover full ML lifecycle
• Emphasize production ML systems
• Similar preparation time investment
• Comparable career impact

Key differences:

AWS ML Specialty:

• Slightly longer exam (180 vs. 120 minutes)
• More questions (65 vs. 50-60)
• Emphasizes SageMaker heavily
• Strong data engineering component
• More widely recognized (larger AWS market share)

Google Professional ML Engineer:

• Stronger MLOps and pipeline focus
• Emphasizes Vertex AI and BigQuery ML
• More emphasis on responsible AI
• Strong TensorFlow integration
• Better for MLOps-oriented roles

Verdict: Choose based on your cloud platform. If multi-cloud or cloud-agnostic: AWS ML Specialty has broader recognition, but Google certification has stronger MLOps focus.

Choosing Between AWS and Google AI Certifications

Match to Organizational Cloud Strategy

If your organization uses AWS:

• Prioritize AWS certifications
• AWS AI Practitioner for foundation
• AWS ML Specialty for technical roles

If your organization uses Google Cloud:

• Prioritize Google certifications
• Cloud Digital Leader for foundation
• Professional ML Engineer for technical roles

If multi-cloud:

• Start with primary platform certifications
• Consider second platform strategically
• Vendor-neutral certifications (CompTIA AI+, IAPP AIGP) provide flexibility

If cloud-agnostic:

• AWS certifications have broader market recognition
• Consider vendor-neutral options first
• Choose AWS or Google based on career market

Consider Career Goals

For maximum marketability:

• AWS ML Specialty (largest market)
• Can add Google certification later

For cutting-edge ML practices:

• Google Professional ML Engineer
• Strong MLOps and Vertex AI focus

For flexibility:

• Pursue both eventually
• Start with primary platform
• Add second platform certification strategically

Evaluate Technical Focus

If you emphasize:

• SageMaker and AWS ecosystem: AWS ML Specialty
• TensorFlow and Google tools: Google Professional ML Engineer
• MLOps and pipelines: Google has slight edge
• Broad service coverage: AWS has more services
• Big data integration: Both strong; depends on platform

Preparation Strategies

For AWS ML Specialty

Study approach:

1. Complete AWS Skill Builder ML learning plan (40+ hours)
2. Deep dive into SageMaker documentation
3. Build 3-5 hands-on projects covering different ML scenarios
4. Study data engineering on AWS (Glue, EMR, data lakes)
5. Take multiple practice exams
6. Review weak areas identified in practice tests

Critical success factors:

• Extensive hands-on SageMaker experience
• Understanding of ML algorithms and when to use each
• Knowledge of AWS security and governance for ML
• Ability to architect complete ML solutions

Common pitfalls:

• Underestimating exam difficulty
• Insufficient hands-on practice
• Focusing only on SageMaker (exam covers broader ecosystem)
• Not managing 180-minute exam time effectively

For Google Professional ML Engineer

Study approach:

1. Complete Google Cloud Skills Boost ML learning path
2. Take Coursera "Machine Learning with TensorFlow on GCP" specialization
3. Complete Vertex AI labs and tutorials
4. Build end-to-end ML pipeline projects
5. Study MLOps practices and automation
6. Take practice exams and review case studies

Critical success factors:

• Strong Vertex AI hands-on experience
• Understanding of ML pipeline automation
• Familiarity with BigQuery ML and AutoML
• Knowledge of GCP best practices for ML

Common pitfalls:

• Underestimating MLOps emphasis
• Insufficient Vertex AI hands-on practice
• Focusing too much on theory vs. practical implementation
• Not preparing for scenario-based questions

Cost Comparison

AWS Certifications

AI Practitioner: $150 ML Specialty: $300 Total for both: $450

Additional costs:

• Practice exams: $30-80 each
• Online courses: $0-200 (many free options)
• AWS service usage: Free tier covers much practice

Total investment: $450-700

Google Certifications

Professional ML Engineer: $200

Additional costs:

• Practice exams: $30-50
• Online courses: $0-200 (Coursera specialization ~$50)
• GCP service usage: Free tier + credits for learning

Total investment: $200-400

ROI Comparison

Both certifications command similar salaries and career opportunities. AWS ML Specialty may have slight edge due to larger AWS market share, but difference is modest. Choose based on technical platform alignment rather than marginal ROI differences.

Multi-Cloud Certification Strategy

For professionals working across cloud platforms:

Phase 1: Foundation

• Get certified on primary platform first
• Build deep expertise before expanding

Phase 2: Expansion

• Add second platform certification strategically
• Leverage transferable ML knowledge
• Focus on platform-specific services and best practices

Phase 3: Specialization

• Pursue advanced or specialty certifications
• Consider vendor-neutral credentials (IAPP AIGP for governance)

Timing:

• Don't pursue multiple simultaneously
• Allow 6-12 months between major certifications
• Maintain currency through practical use

Maintaining Certifications

AWS Certifications

Validity: 3 years Renewal: Recertification exam required Cost: Same as original exam fee Process: Can renew up to 6 months before expiration

Renewal tips:

• Set calendar reminders 6 months before expiration
• Review new service launches and features
• Take practice exams to assess readiness
• Budget renewal costs annually

Google Certifications

Validity: 2 years Renewal: Recertification exam required Cost: Same as original exam fee ($200) Process: Can register for recertification as expiration approaches

Renewal tips:

• Shorter validity period requires more frequent attention
• Stay current with Google Cloud updates
• Complete ongoing learning through Skills Boost
• Plan recertification into development roadmap

Conclusion

AWS and Google Cloud AI certifications both provide valuable credentials for ML professionals. Choose based on your organization's cloud platform, career goals, and technical interests.

Priority recommendations:

• AWS environment: AWS ML Specialty for technical roles, AI Practitioner for broader workforce
• Google Cloud environment: Professional ML Engineer for technical roles
• Multi-cloud: Start with primary platform, add second strategically
• Cloud-agnostic: AWS ML Specialty for broader market recognition

Both credentials require substantial investment (80-120+ hours preparation) but deliver strong ROI through increased capabilities, career opportunities, and compensation.