शुरू करने से पहले आपको क्या जानना चाहिए
आप शुरू करें

शुरू होता है 4 June 2026 05:48

समाप्त होता है 4 June 2026

00 दिन
00 घंटे
00 मिनट
00 सेकंड
course image

AIOps: CI/CD for AI Systems

AIOps: CI/CD for AI Systems | Pluralsight Unlock the potential of AI systems by mastering the intricacies of CI/CD pipelines tailored specifically for Machine Learning (ML), Artificial Intelligence (AI), and Large Language Models (LLMs). This course on Pluralsight will expertly guide you through deploying models to cloud environments, ensuring s.
via Pluralsight

659 कोर्स


28 minutes

वैकल्पिक अपग्रेड उपलब्ध है

Not Specified

अपनी गति से आगे बढ़ें

Free Trial Available

वैकल्पिक अपग्रेड उपलब्ध है

अवलोकन

DevOps is a concept that has been around a long time and has been applied to ML and, recently, LLMs. In this course, AIOps:

CI/CD for AI Systems, you’ll learn to apply CI/CD, a fundamental concept of DevOps, to ML, AI, and LLMs.

First, you’ll explore how CI/CD pipelines work with ML models. Next, you’ll discover how to deploy to cloud environments using a CI/CD pipeline.

Finally, you’ll learn how to automatically test the pipeline at all stages. When you’re finished with this course, you’ll have the skills and knowledge of CI/CD pipelines needed to produce and apply these pipelines to your own ML/AI/LLM models.

पाठ्यक्रम

  • Introduction to AIOps and CI/CD for AI
  • Overview of DevOps principles
    Introduction to CI/CD concepts
    Importance of CI/CD in AI and ML
  • CI/CD Pipelines for Machine Learning Models
  • Designing a CI/CD pipeline for ML
    Tools and platforms for ML CI/CD (Jenkins, GitLab, etc.)
    Version control and model tracking (Git, DVC)
  • Implementing CI/CD Pipelines for AI
  • Integrating CI/CD with machine learning workflows
    Automating data preprocessing and feature engineering
    Continuous training and validation of models
  • Deploying AI Models with CI/CD
  • Strategies for deploying ML models to cloud environments
    Using cloud services (AWS, Azure, GCP) for model deployment
    Infrastructure as code for AI systems (Terraform, Ansible)
  • CI/CD for Large Language Models (LLMs)
  • Special considerations for LLMs in CI/CD
    Techniques for efficient LLM deployment
    Monitoring and updating deployed LLMs
  • Testing in CI/CD Pipelines
  • Automatic testing frameworks and strategies
    Unit testing and integration testing for AI systems
    Ensuring model performance and reliability through testing
  • Advanced CI/CD Topics
  • Handling data drift and model retraining
    Implementing A/B testing and canary releases for AI models
    Security and compliance in AI CI/CD pipelines
  • Case Studies and Real-world Applications
  • Evaluation of successful CI/CD implementations in AI
    Lessons learned from industry leaders
  • Course Conclusion and Practical Application
  • Best practices for CI/CD in AI development
    Hands-on project: Building a CI/CD pipeline for a sample AI model

द्वारा पढ़ाया गया

David Harris


विषय

Computer Science