Kubeflow Training

Infoshare is the largest tech community in CEE and the organizer of the leading tech conference in Gdańsk. It connects startups, investors, corporations, and innovation enthusiasts. It promotes entrepreneurship, knowledge sharing, and networking. Through events, competitions, and programs, it supports the development of the tech ecosystem in Poland and the region.

The Kubeflow training is an intensive two-day course focused on the practical application of this platform for managing the lifecycle of machine learning models on Kubernetes. The training program is designed so that 80% of the time is dedicated to practical workshops and 20% to theory. Participants will learn how to leverage the full potential of Kubeflow for training, deploying, and monitoring ML models, working on real-world examples and use cases.

• Programmers and data engineers who want to expand their skills in managing the lifecycle of ML models on Kubernetes
• IT specialists who want to use Kubeflow to automate data processing and predictions in their organizations
• Data scientists and data analysts eager to train and deploy ML models in a scalable production environment
• Individuals with a basic knowledge of Python programming, foundational knowledge of machine learning, and basic skills in working with Kubernetes.

• How to configure and manage Kubeflow on Kubernetes
• How to deploy ML models using Kubeflow Serving and monitor their performance
• How to conduct exploratory data analysis and train ML models using Kubeflow Pipelines
• How to integrate Kubeflow with other ML tools and cloud platforms and automate ML processes using CI/CD tools

• DAY 1: INTRODUCTION TO KUBEFLOW AND PLATFORM BASICS
   • Basics of Kubeflow
   • Introduction to Kubeflow and its architecture
   • Installing Kubeflow on Kubernetes

  • DATA MANAGEMENT AND EXPLORATORY DATA ANALYSIS (EDA)
     • Importing and processing data in Kubeflow
     • Conducting EDA using Kubeflow Pipelines

  • TRAINING MODELS IN KUBEFLOW
     • Introduction to training components in Kubeflow
     • Automating model training using Kubeflow Pipelines
     • Training the first model
     • Practical exercises on training a model using a real dataset
     • Analyzing results and evaluating the model

• DAY 2: ADVANCED TECHNIQUES AND PRACTICAL APPLICATIONS
  ADVANCED MODEL TRAINING TECHNIQUES
   • Using custom scripts for model training
   • Utilizing GPUs and computing clusters to accelerate training

  • DEPLOYING AND MONITORING MODELS
     • Deploying models using Kubeflow Serving
     • Monitoring and managing deployed models

  • DEPLOYING AND OPTIMIZING THE MODEL
     • Practical exercises on deploying a Kubeflow model
     • Model optimization and hyperparameter tuning

  • INTEGRATION WITH OTHER TOOLS AND SERVICES (OPTIONAL)
     • Integrating Kubeflow with other ML tools and cloud platforms
     • Using CI/CD tools to automate ML processes

16 h/2 days