Transform your AWS infrastructure into a high-performance AI platform
NVMD turns your cloud into an AI accelerator.
Start your Transformation
Transform your AWS infrastructure into a high-performance AI platform
NVMD turns your cloud into an AI accelerator.
Start your Transformation
Transform your AWS infrastructure into a high-performance AI platform
NVMD turns your cloud into an AI accelerator.
Start your Transformation
Transform your AWS infrastructure into a high-performance AI platform
NVMD turns your cloud into an AI accelerator.
Start your Transformation
Transform your AWS infrastructure into
a high-performance AI platform
NVMD turns your cloud into an AI accelerator.
Start your Transformation
Transform your AWS infrastructure into a high-performance AI platform
NVMD turns your cloud into an AI accelerator.
Start your Transformation
The 3 costly mistakes we fix
🏷️ Poor ML instance sizing
• Forgotten SageMaker instances: One ml.g4dn.xlarge costs $6,900/year if left running
• Wrong CPU vs GPU choice: Significant cost overrun using GPU for non-compatible workloads
• Open Canvas workspaces: $1,500/month waste
⚡ Unoptimized data pipelines
• GPU under-utilization: Often 30% instead of optimal potential
• File Mode vs Pipe Mode: 45min startup for 100GB data vs direct streaming
• I/O bottlenecks that paralyze ML training
🔒 Insufficient AI governance
• GDPR risks: Fines up to 4% of global revenue
• Right to erasure impossible without model versioning
• Projects blocked due to lack of compliance
The 3 costly mistakes we fix
🏷️ Poor ML instance sizing
• Forgotten SageMaker instances: One ml.g4dn.xlarge costs $6,900/year if left running
• Wrong CPU vs GPU choice: Significant cost overrun using GPU for non-compatible workloads
• Open Canvas workspaces: $1,500/month waste
⚡ Unoptimized data pipelines
• GPU under-utilization: Often 30% instead of optimal potential
• File Mode vs Pipe Mode: 45min startup for 100GB data vs direct streaming
• I/O bottlenecks that paralyze ML training
🔒 Insufficient AI governance
• GDPR risks: Fines up to 4% of global revenue
• Right to erasure impossible without model versioning
• Projects blocked due to lack of compliance
The 3 costly mistakes we fix
🏷️ Poor ML instance sizing
• Forgotten SageMaker instances: One ml.g4dn.xlarge costs $6,900/year if left running
• Wrong CPU vs GPU choice: Significant cost overrun using GPU for non-compatible workloads
• Open Canvas workspaces: $1,500/month waste
⚡ Unoptimized data pipelines
• GPU under-utilization: Often 30% instead of optimal potential
• File Mode vs Pipe Mode: 45min startup for 100GB data vs direct streaming
• I/O bottlenecks that paralyze ML training
🔒 Insufficient AI governance
• GDPR risks: Fines up to 4% of global revenue
• Right to erasure impossible without model versioning
• Projects blocked due to lack of compliance
The 3 costly mistakes we fix
🏷️ Poor ML instance sizing
• Forgotten SageMaker instances: One ml.g4dn.xlarge costs $6,900/year if left running
• Wrong CPU vs GPU choice: Significant cost overrun using GPU for non-compatible workloads
• Open Canvas workspaces: $1,500/month waste
⚡ Unoptimized data pipelines
• GPU under-utilization: Often 30% instead of optimal potential
• File Mode vs Pipe Mode: 45min startup for 100GB data vs direct streaming
• I/O bottlenecks that paralyze ML training
🔒 Insufficient AI governance
• GDPR risks: Fines up to 4% of global revenue
• Right to erasure impossible without model versioning
• Projects blocked due to lack of compliance
The 3 costly mistakes we fix
🏷️ Poor ML instance sizing
• Forgotten SageMaker instances: One ml.g4dn.xlarge costs $6,900/year if left running
• Wrong CPU vs GPU choice: Significant cost overrun using GPU for non-compatible workloads
• Open Canvas workspaces: $1,500/month waste
⚡ Unoptimized data pipelines
• GPU under-utilization: Often 30% instead of optimal potential
• File Mode vs Pipe Mode: 45min startup for 100GB data vs direct streaming
• I/O bottlenecks that paralyze ML training
🔒 Insufficient AI governance
• GDPR risks: Fines up to 4% of global revenue
• Right to erasure impossible without model versioning
• Projects blocked due to lack of compliance
The 3 costly mistakes we fix
🏷️ Poor ML instance sizing
• Forgotten SageMaker instances: One ml.g4dn.xlarge costs $6,900/year if left running
• Wrong CPU vs GPU choice: Significant cost overrun using GPU for non-compatible workloads
• Open Canvas workspaces: $1,500/month waste
⚡ Unoptimized data pipelines
• GPU under-utilization: Often 30% instead of optimal potential
• File Mode vs Pipe Mode: 45min startup for 100GB data vs direct streaming
• I/O bottlenecks that paralyze ML training
🔒 Insufficient AI governance
• GDPR risks: Fines up to 4% of global revenue
• Right to erasure impossible without model versioning
• Projects blocked due to lack of compliance
Our Proven Methodology
Phase 2: Performance Optimization
Implementation of monitoring, migration to Pipe Mode, data format optimization, governance frameworks deployment, and setup of Model Registry ensuring performance & compliance.
Phase 1: Infrastructure Health Check
Complete audit of your AWS AI ecosystem, inventory of active ML instances, GPU utilization analysis, pipeline evaluation, AI governance assessment, and identification of quick wins for immediate savings.
Phase 3: Advanced Architecture
Automated MLOps pipelines, high availability, advanced model monitoring, and automated compliance documentation creating a resilient, future-proof AI-native architecture.
Our Proven Methodology
Phase 2: Performance Optimization
Implementation of monitoring, migration to Pipe Mode, data format optimization, governance frameworks deployment, and setup of Model Registry ensuring performance & compliance.
Phase 1: Infrastructure Health Check
Complete audit of your AWS AI ecosystem, inventory of active ML instances, GPU utilization analysis, pipeline evaluation, AI governance assessment, and identification of quick wins for immediate savings.
Phase 3: Advanced Architecture
Automated MLOps pipelines, high availability, advanced model monitoring, and automated compliance documentation creating a resilient, future-proof AI-native architecture.
Our Proven Methodology
Phase 2: Performance Optimization
Implementation of monitoring, migration to Pipe Mode, data format optimization, governance frameworks deployment, and setup of Model Registry ensuring performance & compliance.
Phase 1: Infrastructure Health Check
Complete audit of your AWS AI ecosystem, inventory of active ML instances, GPU utilization analysis, pipeline evaluation, AI governance assessment, and identification of quick wins for immediate savings.
Phase 3: Advanced Architecture
Automated MLOps pipelines, high availability, advanced model monitoring, and automated compliance documentation creating a resilient, future-proof AI-native architecture.
Our Proven Methodology
Phase 2: Performance Optimization
Implementation of monitoring, migration to Pipe Mode, data format optimization, governance frameworks deployment, and setup of Model Registry ensuring performance & compliance.
Phase 1: Infrastructure Health Check
Complete audit of your AWS AI ecosystem, inventory of active ML instances, GPU utilization analysis, pipeline evaluation, AI governance assessment, and identification of quick wins for immediate savings.
Phase 3: Advanced Architecture
Automated MLOps pipelines, high availability, advanced model monitoring, and automated compliance documentation creating a resilient, future-proof AI-native architecture.
Our Proven Methodology
Phase 2: Performance Optimization
Implementation of monitoring, migration to Pipe Mode, data format optimization, governance frameworks deployment, and setup of Model Registry ensuring performance & compliance.
Phase 1: Infrastructure Health Check
Complete audit of your AWS AI ecosystem, inventory of active ML instances, GPU utilization analysis, pipeline evaluation, AI governance assessment, and identification of quick wins for immediate savings.
Phase 3: Advanced Architecture
Automated MLOps pipelines, high availability, advanced model monitoring, and automated compliance documentation creating a resilient, future-proof AI-native architecture.
Our Proven Methodology
Phase 2: Performance Optimization
Implementation of monitoring, migration to Pipe Mode, data format optimization, governance frameworks deployment, and setup of Model Registry ensuring performance & compliance.
Phase 1: Infrastructure Health Check
Complete audit of your AWS AI ecosystem, inventory of active ML instances, GPU utilization analysis, pipeline evaluation, AI governance assessment, and identification of quick wins for immediate savings.
Phase 3: Advanced Architecture
Automated MLOps pipelines, high availability, advanced model monitoring, and automated compliance documentation creating a resilient, future-proof AI-native architecture.
$
Substantial reduction in ML/AI bills
• Idle instance elimination: Significant savings
• Sizing optimization: Major compute cost reduction
⚡
Performance gains
• Notable improvement in model training times
• GPU utilization optimization vs current baseline
• Dramatic startup time reduction with Pipe Mode
$
Substantial reduction in ML/AI bills
• Idle instance elimination: Significant savings
• Sizing optimization: Major compute cost reduction
⚡
Performance gains
• Notable improvement in model training times
• GPU utilization optimization vs current baseline
• Dramatic startup time reduction with Pipe Mode
$
Substantial reduction in ML/AI bills
⚡
Performance gains
$
Substantial reduction in ML/AI bills
• Idle instance elimination: Significant savings
• Sizing optimization: Major compute cost reduction
⚡
Performance gains
• Notable improvement in model training times
• GPU utilization optimization vs current baseline
• Dramatic startup time reduction with Pipe Mode
$
Substantial reduction in ML/AI bills
• Idle instance elimination: Significant savings
• Sizing optimization: Major compute cost reduction
⚡
Performance gains
• Notable improvement in model training times
• GPU utilization optimization vs current baseline
• Dramatic startup time reduction with Pipe Mode
$
Substantial reduction in ML/AI bills
• Idle instance elimination: Significant savings
• Sizing optimization: Major compute cost reduction
⚡
Performance gains
• Notable improvement in model training times
• GPU utilization optimization vs current baseline
• Dramatic startup time reduction with Pipe Mode
How long to see first results?
Quick wins (idle instance shutdown, sizing optimization) are visible from the first week. Major performance gains occur after a few weeks of optimization.
What savings can I expect on my AWS bill?
Savings depend on your current situation. We systematically identify significant optimization opportunities during the initial audit.
How does AI Squad Integration work?
A certified NVMD AWS/ML expert temporarily joins your technical team. They work directly on your projects while optimizing your infrastructure and training your teams.
How long to see first results?
Quick wins (idle instance shutdown, sizing optimization) are visible from the first week. Major performance gains occur after a few weeks of optimization.
What savings can I expect on my AWS bill?
Savings depend on your current situation. We systematically identify significant optimization opportunities during the initial audit.
How does AI Squad Integration work?
A certified NVMD AWS/ML expert temporarily joins your technical team. They work directly on your projects while optimizing your infrastructure and training your teams.
How long to see first results?
Quick wins (idle instance shutdown, sizing optimization) are visible from the first week. Major performance gains occur after a few weeks of optimization.
What savings can I expect on my AWS bill?
Savings depend on your current situation. We systematically identify significant optimization opportunities during the initial audit.
How does AI Squad Integration work?
A certified NVMD AWS/ML expert temporarily joins your technical team. They work directly on your projects while optimizing your infrastructure and training your teams.
How long to see first results?
Quick wins (idle instance shutdown, sizing optimization) are visible from the first week. Major performance gains occur after a few weeks of optimization.
What savings can I expect on my AWS bill?
Savings depend on your current situation. We systematically identify significant optimization opportunities during the initial audit.
How does AI Squad Integration work?
A certified NVMD AWS/ML expert temporarily joins your technical team. They work directly on your projects while optimizing your infrastructure and training your teams.
How long to see first results?
Quick wins (idle instance shutdown, sizing optimization) are visible from the first week. Major performance gains occur after a few weeks of optimization.
What savings can I expect on my AWS bill?
Savings depend on your current situation. We systematically identify significant optimization opportunities during the initial audit.
How does AI Squad Integration work?
A certified NVMD AWS/ML expert temporarily joins your technical team. They work directly on your projects while optimizing your infrastructure and training your teams.
How long to see first results?
Quick wins (idle instance shutdown, sizing optimization) are visible from the first week. Major performance gains occur after a few weeks of optimization.
What savings can I expect on my AWS bill?
Savings depend on your current situation. We systematically identify significant optimization opportunities during the initial audit.
How does AI Squad Integration work?
A certified NVMD AWS/ML expert temporarily joins your technical team. They work directly on your projects while optimizing your infrastructure and training your teams.