Day 7: What Does a Complete AI Platform Actually Look Like?

Over the last 6 days, I’ve learned about:

• GPUs
• Kubernetes
• GPU Scheduling
• Ray
• vLLM

Each solved a specific problem.

But I still had a question:

How do all these pieces fit together in a real AI platform?

The answer became much clearer when I stopped looking at individual tools and started looking at the entire system.

Layer 1: AI Applications Link to heading

At the top are the products users interact with.

Examples:

• Virtual Assistants
• Recommendation Systems
• Fraud Detection
• Content Generation
• IoT Applications

This is the business layer.

Everything below exists to support these applications.

Layer 2: AI Platform Services Link to heading

This is where most AI engineering happens.

The platform is responsible for taking raw data and turning it into production models.

A typical flow looks like:

Data
 ↓
Feature Engineering
 ↓
Training
 ↓
Model Registry
 ↓
Deployment
 ↓
Monitoring

Let’s break it down.

Data Processing Link to heading

Every model starts with data.

Teams need to:

• Collect data
• Clean data
• Label data
• Store data

Without good data, nothing else matters.

Feature Engineering Link to heading

Raw data is rarely useful.

Features are created, transformed, and stored for training and inference.

Many organizations use Feature Stores to manage this layer.

Model Training Link to heading

This is where distributed training systems come into play.

This is the part most people associate with AI.

Behind the scenes:

• GPUs
• Scheduling
• Distributed execution

all become important.

Model Registry Link to heading

Once a model is trained:

Where does it live?

Organizations need:

• Versioning
• Metadata
• Reproducibility

Model registries solve this problem.

Deployment & Inference Link to heading

This is where tools like:

• KServe
• Ray Serve
• vLLM

enter the picture.

The goal is simple:

Serve predictions reliably and efficiently.

Monitoring Link to heading

Production models require monitoring just like applications.

Teams track:

• Latency
• Throughput
• Costs
• Data Drift
• Model Performance

A deployed model is not the end of the journey.

It’s the beginning of operating it.

Layer 3: Infrastructure Link to heading

Everything above depends on infrastructure.

This includes:

Compute Link to heading

• CPUs
• GPUs
• TPUs

Storage Link to heading

• Object Storage
• Data Lakes
• Feature Stores

Orchestration Link to heading

• Kubernetes
• Networking
• Scheduling

Accelerators Link to heading

• NVIDIA GPUs
• TPUs
• Specialized Hardware

This was the primary focus of my first 6 days.

Where The Tools Fit Link to heading

One thing I struggled with initially was understanding where different tools belong.

Here’s the mental model I use now:

No single tool is “the AI platform.”

Each tool solves a specific problem.

What Surprised Me Link to heading

Before starting this series, I thought AI platforms were mostly about machine learning models.

Now I think the harder challenge is everything around the model:

• Infrastructure
• Scheduling
• Training Pipelines
• Deployment
• Monitoring

The model is only one component of the system.

The platform is what makes it usable at scale.

Final Takeaway Link to heading

My biggest takeaway from these 7 days is:

AI Platform Engineering is not a single tool or technology.

It’s the collection of systems that take a model from an experiment to a reliable production service.

Over the last week I focused primarily on the infrastructure side:

• GPUs
• Kubernetes
• Scheduling
• Ray
• vLLM

Next, I want to explore the layer sitting above it:

MLOps.

Questions like:

• How are models trained repeatedly?
• How are experiments tracked?
• How are models versioned?
• How are production models monitored?

That will be the focus of the next learning sprint.

Thanks for following along 🚀