Why Kubernetes for ML workloads in MLOps? - Purpose & Use Cases

The Big Idea

What if your ML models could train themselves without you babysitting every step?

The Scenario

Imagine you have many machine learning models to train and test. You try running each model on your laptop or a single server one by one. You have to manually set up the environment, install packages, and manage resources for each model.

The Problem

This manual way is slow and tiring. You might forget to install a package or use the wrong version. Your laptop can get overloaded and crash. It's hard to keep track of which model is running where, and sharing your work with teammates is a mess.

The Solution

Kubernetes helps by automating how your ML workloads run. It manages resources, runs many models in isolated containers, and keeps everything organized. You can easily scale up or down, share environments, and recover from failures without lifting a finger.

Before vs After

✗ Before

python train_model.py --env=setup_manually
python train_model2.py --env=setup_manually

✓ After

kubectl apply -f ml_training_job.yaml
kubectl apply -f ml_training_job2.yaml

What It Enables

With Kubernetes, you can run many ML tasks reliably and at scale, freeing you to focus on improving your models instead of managing machines.

Real Life Example

A data scientist runs multiple experiments on different datasets simultaneously. Kubernetes automatically assigns resources, restarts failed jobs, and lets the team monitor progress from a single dashboard.

Key Takeaways

Manual ML training is slow, error-prone, and hard to scale.

Kubernetes automates resource management and workload orchestration.

This leads to faster, more reliable, and shareable ML workflows.

Practice

1. What is the primary Kubernetes resource used to run a one-time ML training task?

easy

A. Job

B. Deployment

C. Service

D. ConfigMap

Solution

1. Step 1: Understand Kubernetes resource types

   Jobs are designed to run tasks that complete once, like ML training.

2. Step 2: Match resource to ML training task

   Since training is a one-time batch task, Job is the correct resource.

3. Final Answer:

   Job -> Option A

4. Quick Check:

   One-time ML training = Job [OK]

Hint: Use Job for one-time tasks like training [OK]

Common Mistakes:

• Choosing Deployment which is for long-running services
• Confusing Service with workload resource
• Using ConfigMap which stores config data only

2. Which of the following is the correct YAML snippet to request 2 GPUs in a Kubernetes pod spec?

easy

A. resources:
requests:
cpu: 2

B. resources:
limits:
memory: 2Gi

C. resources:
limits:
nvidia.com/gpu: 2

D. resources:
requests:
gpu: 2

Solution

1. Step 1: Identify GPU resource naming in Kubernetes

   GPUs are requested using the vendor-specific resource name like nvidia.com/gpu.

2. Step 2: Check correct YAML structure for limits

   GPUs are usually set under limits, not requests, with the correct key.

3. Final Answer:

   resources: limits: nvidia.com/gpu: 2 -> Option C

4. Quick Check:

   GPU request uses nvidia.com/gpu under limits [OK]

Hint: GPU requests use 'limits' with 'nvidia.com/gpu' key [OK]

Common Mistakes:

• Using 'gpu' instead of 'nvidia.com/gpu'
• Placing GPU under requests instead of limits
• Confusing CPU or memory keys with GPU

3. Given this Kubernetes Job YAML snippet, what will happen when applied?

apiVersion: batch/v1
kind: Job
metadata:
  name: ml-train
spec:
  template:
    spec:
      containers:
      - name: trainer
        image: ml-image:latest
        command: ["python", "train.py"]
      restartPolicy: Never
  backoffLimit: 3

medium

A. The Job runs the training once and retries up to 3 times on failure

B. The Job runs continuously without stopping

C. The Job will fail immediately due to missing restartPolicy

D. The Job creates a Deployment instead of a batch task

Solution

1. Step 1: Understand Job behavior with backoffLimit

   The backoffLimit sets how many retries happen on failure before Job stops.

2. Step 2: Check restartPolicy and command

   restartPolicy: Never means pods won't restart automatically; Job controller retries pods.

3. Final Answer:

   The Job runs the training once and retries up to 3 times on failure -> Option A

4. Quick Check:

   Job with backoffLimit retries 3 times [OK]

Hint: backoffLimit controls retry count for Job failures [OK]

Common Mistakes:

• Thinking Job runs continuously like Deployment
• Assuming restartPolicy: Never causes immediate failure
• Confusing Job with Deployment resource

4. You deployed an ML model with a Deployment but the pods keep restarting. Which is the most likely cause?

medium

A. The ConfigMap is not mounted

B. The Deployment spec is missing replicas field

C. The Service is not exposing the Deployment

D. The container image is missing or incorrect

Solution

1. Step 1: Analyze pod restart reasons

   Pods restarting often means container crashes, commonly due to bad image or command.

2. Step 2: Check other options relevance

   Missing replicas defaults to 1, Service exposure doesn't cause restarts, ConfigMap missing causes config errors but not always restarts.

3. Final Answer:

   The container image is missing or incorrect -> Option D

4. Quick Check:

   Pod restarts usually mean bad container image [OK]

Hint: Pod restarts often mean container image or command error [OK]

Common Mistakes:

• Assuming missing replicas causes restarts
• Confusing Service exposure with pod health
• Thinking ConfigMap absence always crashes pods

5. You want to deploy an ML model serving system that automatically scales based on CPU usage. Which Kubernetes resource and feature combination is best?

hard

A. DaemonSet to run one pod per node

B. Deployment with Horizontal Pod Autoscaler (HPA)

C. StatefulSet with persistent volumes

D. Job with backoffLimit set to 5

Solution

1. Step 1: Identify resource for long-running model serving

   Deployment manages long-running pods and supports updates.

2. Step 2: Choose scaling feature for CPU-based autoscaling

   Horizontal Pod Autoscaler (HPA) automatically adjusts pod count based on CPU usage.

3. Final Answer:

   Deployment with Horizontal Pod Autoscaler (HPA) -> Option B

4. Quick Check:

   Use Deployment + HPA for scalable model serving [OK]

Hint: Use Deployment + HPA for auto-scaling model serving [OK]

Common Mistakes:

• Using Job which is for batch tasks, not serving
• Choosing StatefulSet which is for stateful apps
• DaemonSet runs pods on all nodes, not for scaling