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Platform Engineering for AI/ML Systems

The explosion of AI has created a new specialization: Platform Engineers who build and maintain infrastructure for machine learning workloads. This guide covers the unique challenges, tools, and skills needed for AI/ML platform engineering.

📚 Essential Resources​

📖 Must-Read Books & Papers​

🎥 Video Resources​

🎓 Courses & Training​

📰 Blogs & Articles​

🔧 Essential Tools & Platforms​

Training Platforms​

Model Serving​

Experiment Tracking​

💬 Communities & Forums​

🏆 Industry Resources​

📊 Benchmarks & Datasets​

🎯 Interview Preparation​

Why AI Platform Engineering is Different​

Unique Challenges​

  1. Resource Intensity

    • GPU costs can be $1-4/hour per card
    • Training jobs can run for days or weeks
    • Memory requirements often exceed traditional apps
    • Data movement costs at scale

  2. Complexity

    • Distributed training coordination
    • Mixed hardware environments (CPUs, GPUs, TPUs)
    • Data pipeline dependencies
    • Model versioning and reproducibility

  3. Dynamic Workloads

    • Burst training jobs
    • Variable inference loads
    • Experimental vs production workloads
    • Resource sharing and prioritization

Core Technical Skills​

GPU Infrastructure Management​

Understanding GPU Architecture:

# Essential GPU monitoring commands
nvidia-smi                          # GPU utilization and memory
nvidia-smi dmon                     # Real-time monitoring
nvidia-smi -l 1                     # Continuous monitoring

# Detailed GPU information
nvidia-smi -q                       # Detailed query
nvidia-smi --query-gpu=gpu_name,memory.total,memory.free --format=csv

# Process management
nvidia-smi pmon                     # Process monitoring
fuser -v /dev/nvidia*              # Find GPU users

GPU Resource Management in Kubernetes:

# GPU resource requests
apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  containers:
  - name: cuda-container
    image: nvidia/cuda:11.0-base
    resources:
      limits:
        nvidia.com/gpu: 2  # requesting 2 GPUs
    env:
    - name: NVIDIA_VISIBLE_DEVICES
      value: "0,1"

Multi-Instance GPU (MIG) Configuration:

# Enable MIG mode
sudo nvidia-smi -mig 1

# Create GPU instances
sudo nvidia-smi mig -cgi 9,14,14,19,19 -C

# List MIG devices
nvidia-smi -L

Resources:

ML Pipeline Infrastructure​

Data Pipeline Architecture:

# Example: Distributed data processing with Ray
import ray
from ray import data

@ray.remote
def preprocess_batch(batch):
    # GPU preprocessing
    return transformed_batch

# Distributed data loading
dataset = ray.data.read_parquet("s3://bucket/training-data")
processed = dataset.map_batches(
    preprocess_batch,
    batch_size=1000,
    num_gpus=0.5  # Fractional GPU allocation
)

Training Pipeline Orchestration:

# Kubeflow Pipeline example
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: training-pipeline-
spec:
  entrypoint: ml-pipeline
  templates:
  - name: ml-pipeline
    dag:
      tasks:
      - name: data-prep
        template: preprocess-data
      - name: training
        dependencies: [data-prep]
        template: distributed-training
      - name: evaluation
        dependencies: [training]
        template: model-evaluation
      - name: deployment
        dependencies: [evaluation]
        template: model-deployment

Feature Store Implementation:

# Feature store abstraction
class FeatureStore:
    def __init__(self, backend="redis"):
        self.backend = self._init_backend(backend)
    
    def get_features(self, entity_ids, feature_names):
        """Retrieve features with caching and fallback"""
        features = self.backend.get_batch(entity_ids, feature_names)
        return self._validate_features(features)
    
    def update_features(self, features_df):
        """Update features with versioning"""
        version = self._get_next_version()
        self.backend.write_batch(features_df, version)

Resources:

Model Serving Infrastructure​

High-Performance Model Serving:

# Triton Inference Server configuration
name: "bert_model"
platform: "pytorch_libtorch"
max_batch_size: 8
input [
  {
    name: "input_ids"
    data_type: TYPE_INT64
    dims: [ -1, 512 ]
  }
]
output [
  {
    name: "predictions"
    data_type: TYPE_FP32
    dims: [ -1, 2 ]
  }
]
instance_group [
  {
    count: 2
    kind: KIND_GPU
    gpus: [ 0, 1 ]
  }
]

Auto-scaling for Inference:

# KEDA autoscaler for GPU workloads
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: gpu-inference-scaler
spec:
  scaleTargetRef:
    name: inference-deployment
  minReplicaCount: 1
  maxReplicaCount: 10
  triggers:
  - type: prometheus
    metadata:
      serverAddress: http://prometheus:9090
      metricName: gpu_utilization_percentage
      threshold: '70'
      query: |
        avg(
          DCGM_FI_DEV_GPU_UTIL{pod=~"inference-deployment-.*"}
        )

Model A/B Testing:

# Traffic splitting for model comparison
class ModelRouter:
    def __init__(self, models_config):
        self.models = models_config
        self.metrics = MetricsCollector()
    
    def route_request(self, request):
        # Determine model based on routing rules
        if request.user_id in self.canary_users:
            model = self.models['canary']
            self.metrics.increment('canary_requests')
        else:
            # Weighted routing
            model = self._weighted_choice(self.models['stable'])
        
        return model.predict(request)

Resources:

AI-Specific Platform Challenges​

Large-Scale Training Infrastructure​

Distributed Training Setup:

# Horovod distributed training configuration
import horovod.torch as hvd

hvd.init()

# Pin GPU to local rank
torch.cuda.set_device(hvd.local_rank())

# Scale learning rate by number of GPUs
optimizer = optim.SGD(model.parameters(),
                      lr=args.lr * hvd.size())

# Wrap optimizer with Horovod
optimizer = hvd.DistributedOptimizer(optimizer,
                                     named_parameters=model.named_parameters())

# Broadcast parameters from rank 0
hvd.broadcast_parameters(model.state_dict(), root_rank=0)

Checkpointing and Recovery:

class TrainingCheckpointer:
    def __init__(self, storage_backend="s3"):
        self.storage = self._init_storage(storage_backend)
        
    def save_checkpoint(self, model, optimizer, epoch, metrics):
        checkpoint = {
            'epoch': epoch,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            'metrics': metrics,
            'timestamp': datetime.now()
        }
        
        # Save to distributed storage
        path = f"checkpoints/epoch_{epoch}.pt"
        self.storage.save(checkpoint, path)
        
        # Maintain only last N checkpoints
        self._cleanup_old_checkpoints(keep_last=3)

Resources:

Data Infrastructure for ML​

Data Lake Architecture:

# Delta Lake for ML data versioning
from delta import DeltaTable

# Create versioned feature table
feature_table = (
    spark.range(0, 1000000)
    .withColumn("features", generate_features_udf())
    .write
    .format("delta")
    .mode("overwrite")
    .option("overwriteSchema", "true")
    .save("/ml/features/user_embeddings")
)

# Time travel for reproducibility
df_v1 = spark.read.format("delta").option("versionAsOf", 1).load(path)

Data Validation Pipeline:

# Great Expectations for data quality
import great_expectations as ge

def validate_training_data(df):
    # Create expectations suite
    expectation_suite = ge.dataset.PandasDataset(df)
    
    # Define expectations
    expectation_suite.expect_column_values_to_not_be_null("label")
    expectation_suite.expect_column_values_to_be_between(
        "feature_1", min_value=0, max_value=1
    )
    
    # Validate
    validation_result = expectation_suite.validate()
    
    if not validation_result.success:
        raise DataQualityError(validation_result)

Resources:

Cost Optimization for AI Workloads​

GPU Utilization Monitoring:

# Custom GPU metrics collector
class GPUMetricsCollector:
    def __init__(self):
        self.prometheus_client = PrometheusClient()
        
    def collect_metrics(self):
        metrics = {
            'gpu_utilization': [],
            'memory_usage': [],
            'power_draw': [],
            'temperature': []
        }
        
        # Collect from all GPUs
        for gpu_id in range(torch.cuda.device_count()):
            handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id)
            
            util = pynvml.nvmlDeviceGetUtilizationRates(handle)
            metrics['gpu_utilization'].append(util.gpu)
            metrics['memory_usage'].append(util.memory)
            
        self.prometheus_client.push_metrics(metrics)

Spot Instance Management:

# Spot instance configuration for training
apiVersion: v1
kind: ConfigMap
metadata:
  name: spot-training-config
data:
  spot-handler.sh: |
    #!/bin/bash
    # Check for spot instance termination notice
    while true; do
      if curl -s http://169.254.169.254/latest/meta-data/spot/termination-time | grep -q .*T.*Z; then
        echo "Spot instance termination notice detected"
        # Save checkpoint
        kubectl exec $POD_NAME -- python save_checkpoint.py
        # Gracefully shutdown
        kubectl delete pod $POD_NAME
      fi
      sleep 5
    done

Resources:

Tools and Platforms​

ML Orchestration Platforms​

Kubeflow

  • Kubernetes-native ML platform
  • Pipeline orchestration
  • Multi-framework support
  • Distributed training operators
# Kubeflow PyTorch Operator example
apiVersion: kubeflow.org/v1
kind: PyTorchJob
metadata:
  name: distributed-training
spec:
  pytorchReplicaSpecs:
    Master:
      replicas: 1
      template:
        spec:
          containers:
          - name: pytorch
            image: training:latest
            resources:
              limits:
                nvidia.com/gpu: 1
    Worker:
      replicas: 3
      template:
        spec:
          containers:
          - name: pytorch
            image: training:latest
            resources:
              limits:
                nvidia.com/gpu: 2

MLflow

  • Experiment tracking
  • Model registry
  • Model serving
  • Multi-framework support
# MLflow integration
import mlflow
import mlflow.pytorch

with mlflow.start_run():
    # Log parameters
    mlflow.log_param("learning_rate", lr)
    mlflow.log_param("batch_size", batch_size)
    
    # Train model
    for epoch in range(epochs):
        train_loss = train_epoch(model, train_loader)
        mlflow.log_metric("train_loss", train_loss, step=epoch)
    
    # Log model
    mlflow.pytorch.log_model(model, "model")

Ray

  • Distributed computing framework
  • Hyperparameter tuning (Ray Tune)
  • Distributed training (Ray Train)
  • Model serving (Ray Serve)
# Ray distributed training
import ray
from ray import train
from ray.train import ScalingConfig

def train_func(config):
    model = create_model(config)
    # Training logic
    return model

trainer = train.TorchTrainer(
    train_func,
    scaling_config=ScalingConfig(
        num_workers=4,
        use_gpu=True,
        resources_per_worker={"GPU": 1}
    )
)
results = trainer.fit()

Monitoring and Observability​

Model Performance Monitoring:

# Model drift detection
class ModelMonitor:
    def __init__(self, baseline_metrics):
        self.baseline = baseline_metrics
        self.alerting = AlertingSystem()
        
    def check_drift(self, current_predictions, ground_truth):
        # Statistical drift detection
        psi = self.calculate_psi(
            self.baseline.distribution,
            current_predictions
        )
        
        if psi > self.drift_threshold:
            self.alerting.send_alert(
                "Model drift detected",
                {"psi": psi, "threshold": self.drift_threshold}
            )
        
        # Performance drift
        current_accuracy = accuracy_score(ground_truth, current_predictions)
        if current_accuracy < self.baseline.accuracy * 0.95:
            self.alerting.send_alert(
                "Model performance degradation",
                {"current": current_accuracy, "baseline": self.baseline.accuracy}
            )

Infrastructure Monitoring Stack:

# Prometheus configuration for ML metrics
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'gpu-metrics'
    static_configs:
      - targets: ['dcgm-exporter:9400']
    
  - job_name: 'training-metrics'
    kubernetes_sd_configs:
      - role: pod
        namespaces:
          names:
          - ml-training
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true

Resources:

Career Path to AI Platform Engineering​

Coming from ML Engineering​

Skills to Develop:

  1. Infrastructure Skills

    • Kubernetes operations
    • Cloud platform expertise
    • Networking and storage

  2. SRE Practices

    • Monitoring and alerting
    • Incident response
    • Capacity planning

  3. Platform Mindset

    • Building for multiple teams
    • API design
    • Documentation

Learning Path:

ML Engineering Background
    ↓
Learn Kubernetes basics (2-4 weeks)
    ↓
Cloud platform certification (4-6 weeks)
    ↓
SRE fundamentals (2-4 weeks)
    ↓
ML platform tools (Kubeflow, MLflow) (4-6 weeks)
    ↓
Production ML systems (ongoing)

Coming from Traditional Platform Engineering​

Skills to Develop:

  1. ML Fundamentals

    • Training vs inference
    • Model architectures
    • Data processing needs

  2. GPU Infrastructure

    • CUDA environments
    • GPU scheduling
    • Distributed training

  3. ML-Specific Tools

    • Experiment tracking
    • Model serving
    • Feature stores

Learning Path:

Platform Engineering Background
    ↓
ML fundamentals course (4-6 weeks)
    ↓
GPU/CUDA basics (2-3 weeks)
    ↓
ML tools and frameworks (4-6 weeks)
    ↓
ML platform projects (ongoing)

Interview Preparation​

Common AI Platform Engineering Questions:

  1. System Design

    • "Design a distributed training platform"
    • "Build a real-time model serving system"
    • "Design a feature store"
    • "Create an ML experimentation platform"

  2. Technical Deep Dives

    • GPU scheduling algorithms
    • Data pipeline optimization
    • Model versioning strategies
    • A/B testing for ML

  3. Troubleshooting Scenarios

    • "Training job OOM errors"
    • "Model serving latency spikes"
    • "GPU underutilization"
    • "Data pipeline failures"

Hands-On Projects:

  1. Build a Kubernetes operator for distributed training
  2. Create a model serving pipeline with monitoring
  3. Implement a feature store with versioning
  4. Design a cost optimization system for GPU workloads

Market Landscape​

Demand and Compensation​

2025 Market Stats:

  • Demand: 400% growth in ML platform engineering roles since 2022
  • Compensation: 20-30% premium over traditional platform engineering
  • Top Companies: OpenAI, Anthropic, Google DeepMind, Meta AI, Tesla

Salary Ranges (US Market):

LevelYearsBase SalaryTotal Comp
Junior0-2$130k-$160k$160k-$220k
Mid2-5$160k-$200k$220k-$350k
Senior5-8$200k-$250k$350k-$500k
Staff8+$250k-$320k$450k-$700k+

Key Companies and Teams​

AI-First Companies:

  • OpenAI - ChatGPT infrastructure
  • Anthropic - Claude infrastructure
  • Stability AI - Stable Diffusion platform
  • Hugging Face - Model hub infrastructure

Big Tech AI Teams:

  • Google - Vertex AI, TPU infrastructure
  • Meta - PyTorch, Research clusters
  • Microsoft - Azure ML, OpenAI partnership
  • Amazon - SageMaker, Bedrock

AI Infrastructure Startups:

  • Weights & Biases - Experiment tracking
  • Determined AI - Training platform
  • Anyscale - Ray platform
  • Modal - Serverless GPU compute

Essential Resources​

Books​

Courses​

Communities​

Blogs and Newsletters​

Open Source Projects​

Certifications​

Key Takeaways​

  1. AI platform engineering is a high-growth specialization with excellent career prospects
  2. Unique challenges require both ML understanding and platform expertise
  3. GPU infrastructure is a critical skill differentiator
  4. Cost optimization is crucial given expensive compute resources
  5. Full-stack knowledge from data pipelines to model serving is valuable
  6. The field is rapidly evolving - continuous learning is essential

Remember: The intersection of AI and platform engineering offers exciting opportunities to work on cutting-edge infrastructure that powers the AI revolution. Focus on building robust, scalable platforms that enable data scientists and ML engineers to innovate faster.