Jon Price

AWS Consultant & DevOps Specialist. 15+ years enterprise infrastructure and cloud consulting.

16 minute read

Enterprise AI/ML infrastructure on AWS enables 65% of generative AI projects to reach production in 45 days through proven architectural patterns, comprehensive MLOps pipelines, and optimized cost management strategies. This guide explores real-world frameworks for building scalable, secure, and cost-effective AI infrastructure using AWS Bedrock, SageMaker, and supporting services.

Generative AI has emerged as the #1 IT budget priority for 2025 (45% of organizations, surpassing even security), yet most organizations struggle to move beyond experimentation. The gap between AI experimentation (average 45 projects per organization) and production deployment (only 20 reach production) represents the critical infrastructure challenge enterprise organizations face today.

The Business Case for Enterprise AI Infrastructure

The $100M Generative AI Opportunity

AWS Generative AI Innovation Center Insights:

In partnership with hundreds of enterprises, AWS has identified consistent patterns in successful AI transformations:

Successful AI Organizations (Top 20%):

  • Production deployment rate: 65% of AI experiments reach production
  • Time to production: 45-60 days from concept to production deployment
  • ROI realization: Positive ROI within 6-9 months of production deployment
  • Infrastructure investment: $150K-500K for enterprise-grade AI platform
  • Business impact: $2M-10M annual value creation per successful AI application

Struggling AI Organizations (Bottom 50%):

  • Production deployment rate: 10-15% of experiments reach production
  • Time to production: 6-12 months of experimentation without deployment
  • ROI realization: Negative ROI, continued investment without business outcomes
  • Infrastructure approach: Ad-hoc, project-by-project infrastructure decisions
  • Business impact: $500K+ spent with minimal business value delivered

The Difference: Enterprise AI Infrastructure

Organizations that invest in purpose-built AI infrastructure achieve 4-5x higher production deployment rates and 10x faster time-to-production compared to those attempting to retrofit existing infrastructure for AI workloads.

Real-World AI Infrastructure Results

Case Study: Financial Services Company (Series D, 500 Employees)

Initial State:

  • 12 generative AI pilot projects in experimentation phase
  • No standardized infrastructure or MLOps practices
  • Each data science team building custom deployment pipelines
  • 18-month timeline to production for first AI application
  • $400K invested with zero production deployments

Post-Infrastructure Implementation (9 Months):

  • Enterprise AI platform supporting 25+ concurrent AI projects
  • Standardized MLOps pipeline reducing deployment time to 30 days
  • 8 AI applications in production serving 50K+ daily users
  • Automated cost management reducing GPU costs by 55%
  • $2.3M annual business value from production AI applications

Business Impact:

  • $1.9M net annual value after infrastructure investment
  • 16x faster time to production (18 months → 45 days)
  • 4x production deployment rate (0% → 65%)
  • 55% infrastructure cost reduction through optimization
  • ROI achieved in 8 months from infrastructure investment

Strategic Business Benefits of AI Infrastructure

Competitive Advantage:

  • First-mover advantage in AI-powered product features
  • Operational efficiency through AI-driven automation
  • Enhanced customer experience via personalization and intelligence
  • Data-driven decision making at enterprise scale

Talent Attraction and Retention:

  • Data scientists and ML engineers attracted to strong AI platform
  • Reduced frustration from infrastructure obstacles and slow deployments
  • Focus on model development instead of infrastructure engineering
  • Career development through production AI experience

Risk Management:

  • Centralized governance for AI ethics and compliance
  • Standardized security controls for AI workloads
  • Cost management preventing runaway GPU expenses
  • Audit trails and model versioning for regulatory requirements

AWS AI/ML Infrastructure Architecture

Four-Layer Enterprise AI Architecture

Layer 1: Infrastructure Foundation

  • Compute: EC2 with GPU instances (p4d, p5), SageMaker training and inference
  • Storage: S3 data lakes, FSx for Lustre high-performance storage, EFS for shared datasets
  • Networking: VPC with private subnets, VPC endpoints for AWS services, Direct Connect for hybrid
  • Security: IAM policies, KMS encryption, VPC isolation, GuardDuty threat detection

Layer 2: AI/ML Platform Services

  • Amazon Bedrock: Managed generative AI with foundation models (Claude, Llama, Titan)
  • Amazon SageMaker: End-to-end ML platform for training, deployment, and monitoring
  • AWS Step Functions: ML workflow orchestration and pipeline management
  • Amazon ECR: Container registry for custom ML model containers
  • AWS Batch: Distributed training for large-scale model development

Layer 3: MLOps and Governance

  • SageMaker Pipelines: CI/CD for machine learning workflows
  • SageMaker Model Registry: Model versioning, approval workflows, lineage tracking
  • SageMaker Model Monitor: Drift detection, quality monitoring, bias detection
  • AWS CodePipeline: Infrastructure and model deployment automation
  • Amazon EventBridge: Event-driven ML workflow triggers

Layer 4: Application Integration

  • API Gateway: REST/HTTP APIs for model inference endpoints
  • Lambda: Serverless inference for low-latency, bursty workloads
  • ECS/EKS: Container orchestration for complex ML application deployments
  • Amazon ElastiCache: Caching layer for frequently requested predictions
  • CloudFront: CDN for low-latency global model serving

AWS Bedrock Enterprise Architecture Pattern

When to Use Amazon Bedrock:

  • Generative AI applications (text generation, summarization, chat, code generation)
  • Rapid prototyping and production deployment (weeks not months)
  • No desire to manage foundation model infrastructure
  • Need for multiple foundation model options (Claude, Llama 3, Mistral, Titan)
  • Compliance requirements requiring AWS-managed services

Bedrock Enterprise Implementation:

Architecture Components:

Application → API Gateway → Lambda (Bedrock SDK)
              ↓
          Amazon Bedrock (Claude 3.5 Sonnet)
              ↓
       Model Customization (optional)
       - Fine-tuning with private data
       - Continued pre-training for domain expertise
              ↓
          S3 (Training Data, Logs)
              ↓
       CloudWatch (Monitoring, Alerting)

Cost Optimization Strategies:

  • Model selection: Claude 3 Haiku for simple tasks ($0.25/1M input tokens)
  • Caching: Bedrock prompt caching reduces repeat costs by 90%
  • Batch processing: Asynchronous invocations for non-real-time workloads
  • Request optimization: Minimize tokens through prompt engineering
  • On-demand vs. provisioned: Provisioned throughput for predictable, high-volume workloads

Security and Compliance:

  • Private VPC endpoints (no internet traffic for Bedrock API calls)
  • KMS encryption for data at rest and in transit
  • IAM policies with least-privilege access
  • CloudTrail logging for audit and compliance
  • Bedrock Guardrails for content filtering and safety

Real-World Performance Metrics:

  • Inference latency: 500ms-2s for typical generative AI requests
  • Throughput: 10K+ requests per minute with auto-scaling
  • Availability: 99.9% SLA with multi-AZ deployment
  • Cost: $0.003-0.015 per request depending on model and token count

Amazon SageMaker MLOps Architecture

When to Use Amazon SageMaker:

  • Custom ML model development (computer vision, NLP, forecasting, recommendation)
  • Fine-tuning open-source or proprietary foundation models
  • Large-scale distributed training (multi-GPU, multi-node)
  • A/B testing and canary deployments for ML models
  • Advanced model monitoring and drift detection requirements

End-to-End SageMaker Architecture:

Training Pipeline:

S3 (Training Data) → SageMaker Processing (Data Prep)
  → SageMaker Training (Multi-GPU/Multi-Node)
  → SageMaker Model Registry (Versioning)
  → Manual/Automated Approval
  → SageMaker Endpoint (Production Deployment)

Inference Architecture Options:

Real-Time Inference (SageMaker Endpoints):

  • Use case: User-facing applications requiring <100ms latency
  • Scaling: Auto-scaling based on request rate or custom metrics
  • Cost model: Per-instance-hour pricing ($0.50-$30/hour depending on instance type)
  • Best for: High-throughput, low-latency prediction services

Batch Transform:

  • Use case: Large dataset inference without real-time requirements
  • Scaling: Automatically provisions and de-provisions infrastructure
  • Cost model: Pay only for inference duration (no idle costs)
  • Best for: Periodic batch predictions, data pipeline integration

Serverless Inference:

  • Use case: Intermittent or unpredictable traffic patterns
  • Scaling: Automatic scale-to-zero when idle, instant scale-out
  • Cost model: Pay per request ($0.20 per 1M requests + compute time)
  • Best for: Development environments, low-traffic production endpoints

Asynchronous Inference:

  • Use case: Large payload inference (images, videos, documents)
  • Scaling: Queue-based processing with configurable concurrency
  • Cost model: Per-instance-hour with auto-scaling policies
  • Best for: Document processing, video analysis, large-scale predictions

GPU Instance Selection and Optimization

AWS GPU Instance Types for AI/ML:

P5 Instances (Latest Generation, Most Powerful):

  • Use case: Large language model training, multi-GPU distributed training
  • Specifications: NVIDIA H100 GPUs, 640GB GPU memory, 3.2 Tbps networking
  • Performance: 4x faster than P4d for transformer model training
  • Cost: $98/hour for p5.48xlarge (8 GPUs)
  • When to use: Cutting-edge research, largest model training, time-critical projects

P4d Instances (Production Workhorse):

  • Use case: General-purpose ML training and fine-tuning
  • Specifications: NVIDIA A100 GPUs, 320GB GPU memory, 400 Gbps networking
  • Performance: Excellent for most enterprise ML workloads
  • Cost: $32/hour for p4d.24xlarge (8 GPUs)
  • When to use: Most production ML training workloads (best price/performance)

P3 Instances (Cost-Effective Training):

  • Use case: Model development, experimentation, smaller models
  • Specifications: NVIDIA V100 GPUs, 16GB GPU memory per GPU
  • Performance: 50% performance of P4d but significantly lower cost
  • Cost: $12/hour for p3.8xlarge (4 GPUs)
  • When to use: Development/testing, cost-sensitive training workloads

G5 Instances (Inference Optimization):

  • Use case: Model inference, real-time predictions, graphics rendering
  • Specifications: NVIDIA A10G GPUs optimized for inference
  • Performance: High throughput for inference at lower cost than training instances
  • Cost: $1.62/hour for g5.2xlarge (1 GPU)
  • When to use: SageMaker endpoints, real-time inference, cost-optimized serving

Cost Optimization Strategies:

Spot Instances for Training (70% Cost Reduction):

  • Use managed spot training in SageMaker (automatic checkpoint and resume)
  • Typical savings: 70-85% vs. on-demand pricing
  • Interruption handling: SageMaker automatically resumes from last checkpoint
  • Best for: Training jobs >30 minutes with checkpointing support

Right-Sizing GPU Instances:

  • Monitor GPU utilization (target 70-85% for training, 60-75% for inference)
  • Scale down instance types when GPU memory not fully utilized
  • Use multi-model endpoints to share GPU across multiple models (5-10x cost reduction)

Reserved Instances for Production:

  • 1-year Reserved Instances: 40% savings vs. on-demand
  • 3-year Reserved Instances: 60% savings vs. on-demand
  • Use for stable production inference endpoints running 24/7

Implementation Strategy: 45-Day Path to Production

Phase 1: Foundation and Platform Setup (Days 1-15)

Week 1: Architecture Design and Environment Setup

Key Activities:

  • Define AI use cases and business value hypotheses
  • Design multi-account AWS architecture (AI development, staging, production)
  • Implement security baseline (IAM policies, VPC configuration, encryption)
  • Deploy initial infrastructure using IaC (Terraform or CloudFormation)

Infrastructure Deliverables:

  • AWS Landing Zone with dedicated AI/ML accounts
  • S3 data lake with appropriate lifecycle policies
  • VPC configuration with private subnets and VPC endpoints
  • IAM roles for data scientists, ML engineers, and applications

Success Criteria:

  • Data scientists can provision SageMaker notebooks within 5 minutes
  • Data accessible in S3 with appropriate security controls
  • Network connectivity established for all required AWS services

Week 2: MLOps Pipeline and Tooling

Key Activities:

  • Deploy SageMaker Pipelines for model training orchestration
  • Implement Model Registry for version control and approval workflows
  • Configure CI/CD pipelines for infrastructure and model deployment
  • Establish monitoring and logging infrastructure (CloudWatch, X-Ray)

Tooling Deliverables:

  • SageMaker Studio environment for all data science teams
  • CI/CD templates for common ML workflows (training, deployment, monitoring)
  • Jupyter notebook templates for standardized model development
  • Cost tracking and allocation tags for AI/ML resources

Success Criteria:

  • Data scientists can launch training jobs with 3 clicks
  • Model training results automatically logged to Model Registry
  • Cost visibility by project, team, and environment
  • Standardized notebook templates accelerate development

Phase 2: Model Development and Training (Days 16-30)

Week 3: Data Preparation and Model Experimentation

Key Activities:

  • Ingest and prepare training datasets in S3 data lake
  • Conduct model experimentation with multiple algorithms and hyperparameters
  • Implement data versioning and lineage tracking
  • Establish baseline model performance metrics

Data Science Deliverables:

  • Clean, validated training datasets with documentation
  • 3-5 trained model candidates with performance comparisons
  • Hyperparameter tuning results and optimal configurations
  • Model explainability analysis and documentation

Performance Targets:

  • Model accuracy/F1/RMSE meets business requirements
  • Inference latency <200ms for 95th percentile
  • Model size optimized for cost-effective deployment
  • Bias and fairness analysis completed

Week 4: Model Optimization and Validation

Key Activities:

  • Optimize model for inference performance (quantization, pruning)
  • Validate model on holdout test data and real-world scenarios
  • Conduct A/B testing framework implementation
  • Prepare model deployment artifacts and documentation

Engineering Deliverables:

  • Production-ready model artifacts in Model Registry
  • Inference API specification and performance benchmarks
  • Load testing results demonstrating scale requirements
  • Model monitoring plan with drift detection thresholds

Validation Criteria:

  • Model performance meets or exceeds baseline requirements
  • Inference latency and throughput validated under production load
  • Security scanning completed for model dependencies
  • Documentation sufficient for operations team handoff

Phase 3: Production Deployment and Monitoring (Days 31-45)

Week 5: Canary Deployment and Initial Rollout

Key Activities:

  • Deploy model to production SageMaker endpoint (10% traffic)
  • Implement API Gateway and Lambda integration for application access
  • Configure monitoring dashboards and alerting rules
  • Conduct production smoke testing and validation

Production Deliverables:

  • Production inference endpoint with auto-scaling configuration
  • API Gateway with authentication, throttling, and caching
  • CloudWatch dashboards showing inference metrics and costs
  • PagerDuty/SNS alerting for model performance degradation

Deployment Validation:

  • 10% canary traffic shows no performance degradation
  • Inference latency P95 <200ms in production environment
  • Error rate <0.1% for initial production requests
  • Model predictions match validation dataset expectations

Week 6-7: Full Production Rollout and Optimization

Key Activities:

  • Gradually increase traffic from 10% → 50% → 100%
  • Monitor model performance, latency, and cost metrics
  • Implement cost optimization based on production usage patterns
  • Establish model retraining and versioning processes

Operational Deliverables:

  • 100% traffic routed to new model endpoint
  • Model monitoring dashboard tracking drift and performance
  • Automated retraining pipeline triggered by drift detection
  • Runbook for common operational scenarios (scaling, rollback, updates)

Success Metrics (45-Day Mark):

  • Model serving 100% of production traffic
  • P95 latency <200ms with 99.9% availability
  • Cost per inference within planned budget
  • Model performance metrics stable (no significant drift)
  • Operations team trained on monitoring and troubleshooting

AI Workload Cost Management

GPU Cost Optimization Strategies

Training Cost Optimization:

Use Spot Instances for Training (70% Savings):

  • SageMaker managed spot training with automatic checkpoint/resume
  • Typical interruption rate: 5-10% with proper diversification
  • Best practices: Enable checkpointing every 5-10 minutes
  • Result: $3,000 training job becomes $900 with spot (70% savings)

Distributed Training Efficiency:

  • Use data parallelism for large datasets (near-linear scaling to 8 GPUs)
  • Model parallelism for models too large for single GPU
  • Mixed precision training (FP16) for 2-3x throughput improvement
  • Gradient accumulation to simulate larger batch sizes without memory increase

Training Instance Right-Sizing:

  • Monitor GPU memory utilization (target 80-90%)
  • Use smaller instance types if GPU memory underutilized
  • Batch size optimization to maximize GPU utilization
  • Profile training jobs to identify CPU/GPU bottlenecks

Real-World Example:

  • Inefficient: p4d.24xlarge (8 GPUs) at 40% utilization = $32/hour wasted $19/hour
  • Optimized: p4d.8xlarge (4 GPUs) at 80% utilization = $11/hour, same training time
  • Savings: 65% cost reduction through right-sizing

Inference Cost Optimization:

Multi-Model Endpoints (5-10x Cost Reduction):

  • Deploy 5-10 models on single SageMaker endpoint
  • Models loaded dynamically based on request
  • Shared GPU memory across models
  • Use case: Multiple similar models or A/B testing scenarios
  • Cost: $1.62/hour (g5.2xlarge) serves 10 models instead of 10 endpoints at $16.20/hour

Serverless Inference for Intermittent Traffic:

  • Auto scale-to-zero when no traffic (no idle costs)
  • Cold start: 5-10 seconds for first request
  • Use case: Development environments, low-traffic APIs
  • Cost comparison: $0 idle vs. $1.62/hour for dedicated endpoint (94% savings for 4 hours daily usage)

Inference Caching:

  • Implement ElastiCache for frequently requested predictions
  • 80% cache hit rate = 80% reduction in model invocations
  • Sub-millisecond cache latency vs. 50-200ms model inference
  • Cost: $50/month for cache vs. $1,200/month for inference (95% savings)

AWS Bedrock Cost Management

Token Optimization Strategies:

Prompt Engineering for Cost Efficiency:

  • Minimize input tokens: Clear, concise prompts without unnecessary context
  • Use Claude 3 Haiku for simple tasks ($0.25/1M tokens vs. $3/1M for Sonnet)
  • Implement prompt caching for repeated context (90% cost reduction)
  • Chain-of-thought prompting only when necessary (increases token count)

Model Selection Framework:

Simple Classification/Extraction: Claude 3 Haiku ($0.25/1M input tokens)
General-Purpose Chat/Analysis: Claude 3.5 Sonnet ($3/1M input tokens)
Complex Reasoning/Coding: Claude 3 Opus ($15/1M input tokens)

Prompt Caching (90% Cost Reduction for Repeated Context):

  • Cache static context (system prompts, documentation, examples)
  • Cached tokens cost 90% less than processing on every request
  • Use case: Chatbots with consistent personality, RAG with static documents
  • Example: 50K token document + 500 token query = $0.01 vs. $0.10 without caching

Batch vs. Real-Time Processing:

  • Batch inference: 50% discount vs. on-demand pricing
  • Use case: Document processing, content generation pipelines
  • Tradeoff: 12-24 hour latency vs. real-time response

Cost Monitoring and Governance:

Set Up Bedrock Cost Tracking:

  • CloudWatch metrics for token usage by application/team
  • AWS Budgets alerts at 80%, 90%, 100% thresholds
  • Daily cost review for anomalous usage patterns
  • Showback/chargeback by cost allocation tags

Real-World Bedrock Cost Example:

  • Chatbot application: 1M conversations per month
  • Average: 2,000 input tokens + 500 output tokens per conversation
  • Model: Claude 3.5 Sonnet ($3/1M input, $15/1M output)
  • Cost without caching: (1M × 2K × $3/1M) + (1M × 500 × $15/1M) = $6,000 + $7,500 = $13,500/month
  • Cost with prompt caching (50% cache hit): $6,750/month (50% savings)
  • Cost with Haiku for simple queries (60% of traffic): $4,200/month (69% savings)

Security, Compliance, and Governance

AI-Specific Security Considerations

Model Security and Intellectual Property:

  • Encrypt model artifacts at rest (S3 with KMS encryption)
  • Restrict access to Model Registry with IAM policies
  • Use VPC endpoints for SageMaker to prevent internet exposure
  • Implement CloudTrail logging for model access and deployment audit trails

Data Privacy and Protection:

  • Encrypt training data at rest and in transit (TLS 1.2+)
  • Implement data access controls with S3 bucket policies and IAM
  • Use AWS PrivateLink for secure data transfer between accounts
  • Enable S3 Object Lock for immutable training dataset storage (compliance)

Inference Endpoint Security:

  • Deploy inference endpoints in private subnets (no public IP)
  • Use API Gateway with IAM authorization or Lambda authorizers
  • Implement rate limiting and DDoS protection (AWS WAF, Shield)
  • Encrypt inference requests and responses (TLS 1.3)

Secrets Management:

  • Store API keys and credentials in AWS Secrets Manager
  • Rotate secrets automatically every 90 days
  • Use IAM roles instead of API keys where possible
  • Audit secret access with CloudTrail logs

AI Governance Framework

Model Approval Workflow:

  1. Data scientist submits model to Model Registry with documentation
  2. Automated validation: Performance metrics, security scanning, bias analysis
  3. Technical review: ML engineer validates architecture and optimization
  4. Business review: Product manager confirms alignment with requirements
  5. Security review: Security team validates compliance with policies
  6. Final approval: VP Engineering or designated approver authorizes production deployment

Model Monitoring and Drift Detection:

  • SageMaker Model Monitor: Automated data quality and drift detection
  • Custom metrics: Business KPIs and domain-specific metrics
  • Alerting thresholds: Drift >5% triggers investigation, >10% triggers retraining
  • Retraining cadence: Monthly scheduled retraining or triggered by drift

Responsible AI Practices:

  • Bias detection: Regular fairness analysis across demographic segments
  • Explainability: SHAP or LIME analysis for model interpretability
  • Human oversight: Human-in-the-loop for high-stakes predictions
  • Ethics review: Cross-functional AI ethics board for sensitive applications

Compliance for AI Workloads

SOC 2 Type II Compliance:

  • Audit logging: CloudTrail for all AI infrastructure access
  • Access controls: Least-privilege IAM policies, MFA enforcement
  • Data encryption: At-rest and in-transit encryption for all AI data
  • Change management: Approval workflows for model deployment to production

HIPAA Compliance for Healthcare AI:

  • Use HIPAA-eligible AWS services (SageMaker, Bedrock, S3, etc.)
  • Sign Business Associate Agreement (BAA) with AWS
  • Implement audit logging and access controls
  • PHI encryption with KMS customer-managed keys

GDPR Compliance for EU Data:

  • Data residency: Use EU regions (eu-west-1, eu-central-1) for training and inference
  • Right to deletion: Implement data deletion workflows for customer data
  • Consent management: Track and honor user consent for AI processing
  • Data processing agreements: Document AI processing activities

Common AI Infrastructure Challenges and Solutions

Challenge 1: Data Science Team Productivity

Problem: Data scientists spend 60-80% of time on infrastructure instead of model development

Solution: Self-Service AI Platform

  • SageMaker Studio with pre-configured environments
  • One-click infrastructure provisioning (notebooks, training jobs, endpoints)
  • Automated data access with catalog and discovery (AWS Glue)
  • Template libraries for common ML workflows

Result: 3-5x improvement in data scientist productivity, faster time-to-production

Challenge 2: Model Deployment Complexity

Problem: Manual, error-prone deployment processes taking weeks

Solution: Automated MLOps Pipelines

  • SageMaker Pipelines for automated training and deployment
  • GitHub Actions or AWS CodePipeline for CI/CD
  • Infrastructure as Code for reproducible deployments (Terraform, CDK)
  • Automated testing and validation in staging environment

Result: Deployment time reduced from weeks to hours, 90% reduction in deployment errors

Challenge 3: Unpredictable and High AI Infrastructure Costs

Problem: GPU costs exceeding budget, no cost visibility or control

Solution: Comprehensive Cost Management

  • Real-time cost dashboards by project and team
  • Automated right-sizing and spot instance usage
  • Budget alerts and anomaly detection
  • Quarterly cost optimization reviews

Result: 40-60% cost reduction, predictable AI infrastructure spending

Challenge 4: Model Performance Degradation

Problem: Models perform well in development but degrade in production

Solution: Automated Model Monitoring

  • SageMaker Model Monitor for drift detection
  • Real-time performance metrics and alerting
  • Automated retraining triggered by performance degradation
  • A/B testing framework for safe model updates

Result: 95% reduction in undetected model issues, proactive problem resolution

Ready to Build Your Enterprise AI Infrastructure?

Daily DevOps specializes in enterprise AI/ML infrastructure implementations on AWS that accelerate time-to-production from months to weeks while optimizing costs and ensuring governance. Our proven frameworks help you achieve 65% production deployment rates and realize ROI in 6-9 months.

Schedule Your Free AI Infrastructure Assessment:

  • Comprehensive review of your AI/ML use cases and requirements
  • Architecture design recommendations for AWS Bedrock and SageMaker
  • Cost estimation and optimization opportunities
  • MLOps maturity assessment and implementation roadmap

What You’ll Receive:

  • 90-minute consultation with AI infrastructure specialist
  • Detailed assessment report with architecture diagrams
  • 30-60-90 day implementation plan with milestones
  • Custom proposal with investment and ROI projections

Contact Jon Price:

Transform your AI experiments into production value. Let’s build your enterprise AI infrastructure together.

This article is part of our AWS AI/ML and Infrastructure series. For more insights on generative AI, machine learning operations, and AWS best practices, explore our comprehensive resource library and case studies.

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