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hands-on lab

AI Engineer L6 M4 Practise Stage Lab Workshop 2

Difficulty: Intermediate
Duration: Up to 1 hour and 30 minutes
Students: 2
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Description

Lab for Workshop 2: Advanced Programming and ML Libraries

Description of the activity: 

In this lab you will review the basics of main ML library in Python — scikit-learn. You will look at estimators, transformers and pre-processors, and streamlining and automating the workflow of ML projects vis pipelines. You will also look at reproducibility and version control.

This lab allows learners to examine and run available Jupyter notebooks, and to create your own based on tasks given to you. The environment holds datasets (csv files), Jupyter notebook, and instructions. You will be able to download the work to your computer.

Learning objectives

Upon completion of this intermediate level lab, you will be able to:

  • Process automation such as document processing, data extraction
  • Smart scheduling
  • Predictive maintenance

Intended audience

  • Data Engineers
  • DevOps Engineers
  • Machine Learning Engineers
  • Software Engineers

Prerequisites

Familiarity with the following will be beneficial but is not required:

  • Jupyter Notebooks
  • scikit-learn, TensorFlow, or PyTorch
  • Data visualization libraries
Hands-on Lab UUID

Lab steps

0 of 3 steps completed.Use arrow keys to navigate between steps. Press Enter to go to a step if available.
  1. Logging In to the Amazon Web Services Console
  2. Launching Jupyter Lab on SageMaker Notebook
  3. Open Workshop2 Notebook

Lab Rules