Cloud Foundations for Infrastructure AI

Hi Inner Circle,

Welcome to the new series: Infrastructure AI in the Cloud — where we’ll dive into what powers those foundational, task-based models behind the scenes.

There are so many models driving innovation — open-source, closed proprietary, and everything in between.
Sure, you might have a model that does text-to-video generation, but... how do these enterprises actually run those models?

• How do you train a foundational model or fine-tune an existing one?

• What are the compute components you need to know?

• How do you optimize resources for specific tasks — especially when compute is expensive?

• And what about automation, monitoring, management?

If you’ve been searching for clarity on all of that — I’m sharing what I’ve learned so far.

And most importantly — how are cloud providers playing a key role in making this infrastructure scale?

Welcome to Day 1: AI Infrastructure in the Cloud

Let’s start by understanding:

➤ What is Infrastructure AI?

It’s the layer that enables large-scale AI models to actually run — across training, fine-tuning, and deployment. Think:

• GPU/TPU clusters, distributed storage, model orchestration systems

• Networking, autoscaling, containerization (hello, Kubernetes)

• Tooling for observability, logging, cost tracking,

➤ Why Cloud Is Core to AI Infrastructure

AI workloads are compute-intensive, dynamic, and resource-hungry — the cloud is built to handle that.

• Elasticity: Spin up 100 GPUs for training, scale down when done

• Global reach: Serve inference close to users with low latency

• Scalability & reliability: Cloud-native systems are designed for both

  and so much more

➤ Some Practical Components of Cloud-Native AI

Building scalable, production-ready AI in the cloud involves several interdependent layers:

1. AI Workloads

• Data Processing: Getting data ready for AI.

• Training: Building and teaching AI models.

• Inference: Using AI models to make predictions.

• Retraining & Evaluation: Making AI models smarter with new data.

2. ML Lifecycle Components

• Data Ingestion: Getting data into the system ~ from cloud storage, APIs, or real-time streams

• Training Pipelines: Automated steps for building AI models ~ e.g. versioning, checkpointing

• Experiment Tracking: Keeping tabs on AI model experiments with tools like MLflow, Weights & Biases etc

• Model Registry & CI/CD: Storing and automatically deploying AI models.

• Monitoring: Watching AI models to make sure they're working right ~ track model drift, latency, performance

3. Open Software Stack

• Libraries: These are pre-written sets of code like Hugging Face Transformers, TensorRT, etc that help you quickly add specific AI capabilities to your projects.

• Frameworks: These are the foundational software systems, such as PyTorch, TensorFlow, JAX, and Keras, that provide the structure and tools for building and training AI models.

• MLOps Tools: These are specialized tools like MLflow, DVC etc that help manage and automate the entire lifecycle of AI models, from development to deployment and monitoring.

4. Orchestration

• Distributed Jobs: These are systems like Kubernetes, Ray, and Slurm that help run AI tasks across many nodes at scale.

• Workflow Engines: These are tools like Kubeflow Pipelines, Airflow, and Metaflow that automate and manage the step-by-step process of building and deploying AI models.

5. Infrastructure Layer

• Deployment Options:

  • Cloud-native (AWS, GCP, Azure, also neo clouds)

  • Hybrid/on-prem (for sensitive or low-latency use cases)

• Containerization: Docker

• Infrastructure-as-Code: Terraform, Pulumi, Helm

i. Compute

• GPUs: NVIDIA H100, A100, L4 for training/inference

  • Choose spot or on-demand based on workload needs

• TPUs: Google’s accelerators for ML-based workloads

• CPUs: For lightweight preprocessing or inference

• Bare metal clusters

ii. Storage

• Object Storage: For datasets, artifacts

• Block Storage: High I/O workloads

• File Systems: NFS, Lustre — for distributed training checkpoints

• Blob Stores: Model weights, logs, configs

iii. Networking

• High-throughput, low-latency interconnects

• Private VPCs for secure, fast intra-cluster communication

• Cloud-provider specific networking stack

Hardware Layer

• GPU nodes, TPU pods, custom chips

• Storage accelerators (NVMe)

• Smart NICs / DPUs for offloading networking overhead

Model Hosting

• Inference Servers:

  • Triton Inference Server (multi-framework)

  • TorchServe (PyTorch) etc

• Cloud-native Endpoints:

  • AWS SageMaker, GCP Vertex AI, Azure ML

• Deployment Patterns: A/B testing, canary rollout, multi-model endpoints

Two Ways to Operate

Self-Managed Infrastructure

Use Cases:

• Foundational training

• Custom parallelism, GPU scheduling, and sharding

• Using custom frameworks or libraries

• Avoiding dependency on vendor abstractions

You're responsible for:

• GPU provisioning, scaling, scheduling

• Checkpointing, resume logic, retries

• Cost optimization, auto-scaling

• Observability, logging, compliance, and security

Managed Services

Ideal for fine-tuning, inference, and rapid experimentation.

Use Cases:

• Fine-tuning open-source models on private data

• Batch or real-time inference

• Building and testing ML workflows quickly

• Startups or teams without dedicated infra ops

Managed for you:

• Compute provisioning + auto-scaling

• Data ingestion pipelines

• Experiment tracking + model registry

• Endpoint hosting, rollback, monitoring

• Cost controls, security, quota limits

Where Cloud Providers Add Strategic Value

Cloud isn’t just servers and GPUs — it’s an ecosystem that accelerates AI delivery.

• Pretrained models & APIs

• Custom chips

• Private networking for AI clusters

• Tight integration with cloud-native data warehouses

That’s it for today!
If anything above felt unclear, don’t worry — we’ll dig deeper into specific workloads and their compute/storage requirements over the next few days.

Stay tuned!

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