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Module 7: AI/LLM/ML Ops for Beginners

L7: Kubeflow

L7: Kubeflow

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What is Kubeflow?

Kubeflow is an open-source MLOps platform built on top of Kubernetes that helps in building, training, deploying, and managing Machine Learning workflows at scale.

In simple terms:

Kubeflow is like a factory that runs and manages ML pipelines on Kubernetes automatically.

Key Components of Kubeflow

1. Kubeflow Pipelines

  • Build end-to-end ML workflows

  • Automate data processing, training, evaluation, and deployment

  • Supports versioning and experiment tracking

2. Katib

  • Performs hyperparameter tuning

  • Automatically finds best model parameters

3. KFServing / KServe

  • Deploys ML models as scalable APIs

  • Supports TensorFlow, PyTorch, Scikit-learn, XGBoost

4. Notebooks

  • Jupyter-based development environment

  • Used by Data Scientists for experimentation

5. Metadata & Experiments

  • Tracks model versions, runs, metrics, and artifacts

Features of Kubeflow

  • Native Kubernetes integration

  • Scalable and distributed training

  • Automated ML pipelines

  • Multi-framework support

  • Reproducible experiments

  • Cloud-agnostic (works on AWS, GCP, Azure, on-prem)

Benefits of Kubeflow

  1. Full MLOps lifecycle management

  2. Easy pipeline automation

  3. Efficient resource utilization

  4. Supports team collaboration

  5. Production-ready model deployment

  6. Reduces manual ML operations

Real-World Use Cases

  • Large-scale model training (Computer Vision, NLP)

  • Continuous model retraining pipelines

  • Hyperparameter tuning at scale

  • Enterprise ML platforms

  • GenAI model orchestration

Kubeflow in AWS Ecosystem

  • Runs on Amazon EKS (Kubernetes Service)

  • Uses S3 for data storage

  • Integrates with SageMaker for training & inference

  • Uses CloudWatch for monitoring

Quiz

Q1. What is the main purpose of Kubeflow?

A. To store datasets
B. To manage Kubernetes clusters
C. To run and manage end-to-end ML workflows on Kubernetes
D. To build mobile applications

Correct Answer: C
Explanation: Kubeflow is designed to automate and manage the full ML lifecycle (training, pipelines, deployment, monitoring) on Kubernetes.

Q2. Which Kubeflow component is used for hyperparameter tuning?

A. KServe
B. Katib
C. Pipelines
D. Notebooks

Correct Answer: B
Explanation: Katib is Kubeflow’s component for automatic hyperparameter tuning and model optimization.