watsonx Code Assistant for Red Hat Ansible Lightspeed: Generative AI Training (W7S174G-SPVC)

Overview

The idea of automating the generation of Ansible Playbook code with artificial intelligence (AI) stems from the challenges and bottlenecks often faced by developers who are tasked with traditional, manual creation of Playbooks. Developers and programmers must craft precise, error-free Playbooks which are potentially automating jobs across vast collections of assets or hardware. One of the benefits of automation is being able to perform such tasks at scale. Conversely, performing tasks at scale also poses one of the greatest risks of automation: that if things fail, they can fail rapidly and across vast swathes of the IT estate. It should come as no surprise, then, that authoring Playbooks often demands technical expertise and a deep understanding of the targeted systems and services which Ansible is to automate. IBM® watsonx Code Assistant™ for Red Hat® Ansible® Lightspeed interprets natural language prompts from users about the code and tasks they wish to generate, which, in turn, are translated by AI models into the necessary Ansible Task code.Across 4 hours of self-paced learning, participants go hands-on with watsonx Code Assistant for Red Hat Ansible Lightspeed to learn skills that are necessary to demonstrate Ansible Playbook task creation.

Audience

This course is intended for data scientists, AI specialists, watsonx specialists, solution architects, and anyone that wants to learn more about watsonx Code Assistant for Red Hat Ansible Lightspeed.

Prerequisites

null

Objective

After completing this course, you will be able to:

  • Build automation jobs.
  • Customize recommendation models.
  • Use the generative AI capabilities of watsonx Code Assistant for Red Hat Ansible Lightspeed

 

Detaylari Göster

Course Outline

Course outline:

  • Course introduction
  • Unit 1 Generating code
  • Exercise 1 Generating code
  • Unit 2 Content source matching and post-processing
  • Exercise 2: Content source matching and post-processing
  • Unit 3 Task description tuning and model customization
  • Exercise 3 Task description tuning and model customization
Download With Schedule Download Without Schedule