In this course, we will show you how to use KBC for developing, experimenting, registering, and deploying your Machine Learning models.

Specifically, we show you how to set up your Python / R workspace, integrate with Git, experiment with MLFlow, and deploy your models on your choice of model depository, or on Keboola's infrastructure.  

Those with experience with writing models or working in a data science environment will be helpful.

The Data Science course is a part of these certificates: Data Scientist

Total Video Lessons Length: 59 Minutes

Time for your notes, coffee breaks, video replay: N/A (individual)

Assignment Length: individual (2 assignments)

Course curriculum

  • 1

    Introduction

    • Shared Project -- Watch First

    • Introduction
  • 2

    Workspaces

    • Workspaces

    • Demo - Introduction

    • Demo - Creating a Workspace

    • Demo - Loading Data and Connecting to a Workspace

    • Demo - Additional Features

    • Demo - JupyterLab Tour
  • 3

    Experiments and Development

    • Workflow

    • MLFlow
  • 4

    MLFlow Demo

    • Running Experiments

    • Register a Model

    • Deploy and Use a Model
  • 5

    Assignment

    • Assignment Overview

    • Assignment
  • 6

    Resources

    • Presentation
  • 7

    Before you go ...

    • Course Feedback