Cloud Computing and MLOps: ML Pipelines

Artificial Intelligence    |    Intermediate
  • 11 videos | 26m 24s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 30 users Rating 4.3 of 30 users (30)
ML pipelines help organizations improve the standards of machine learning (ML) models, improve their business strategy, and reduce redundant work and miscommunication. They consist of a series of ML workflow steps performed in a connected and automated/semi-automated way. Through this course, learn the basics of ML pipelines. Discover the uses and benefits of ML pipelines and the characteristics of manual and automated pipelines. Next, explore best practices for building pipelines and the three types of environments in the MLOps process. Finally, examine the importance of CI/CD in ML, the purpose of ML pipeline testing, and ML pipeline testing tools and frameworks. Upon completion, you'll be able to define ML pipelines and their benefits.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline the concept and use cases of ml pipelines
    Identify the characteristics of manual ml pipelines
    Name automated ml pipeline characteristics
    Recognize best practices for preparing and building an ml pipeline
    Outline the three types of environments in the mlops process
  • State the importance of ci/cd in ml
    Recognize the use cases and technologies for ci/cd in machine learning (ml)
    Outline the purpose and need for ml pipeline testing
    List ml pipeline testing tools and frameworks
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 43s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 2m 14s
    Upon completion of this video, you will be able to outline the concept and use cases of ML pipelines. FREE ACCESS
  • Locked
    3.  Manual Pipelines
    1m 19s
    After completing this video, you will be able to identify the characteristics of manual ML pipelines. FREE ACCESS
  • Locked
    4.  Automated Pipelines
    1m 27s
    In this video, we will name automated ML pipeline characteristics. FREE ACCESS
  • Locked
    5.  ML Pipeline Preparation and Build Best Practices
    2m 32s
    In this video, recognize best practices for preparing and building an ML pipeline. FREE ACCESS
  • Locked
    6.  Development, Staging, and Production Environments
    2m 12s
    Upon completion of this video, you will be able to outline the three types of environments in the MLOps process. FREE ACCESS
  • Locked
    7.  CI/CD Pipelines
    2m 50s
    In this video, we will state the importance of CI/CD in ML. FREE ACCESS
  • Locked
    8.  Use Cases for CI/CD
    3m 5s
    In this video, recognize the use cases and technologies for CI/CD in machine learning (ML). FREE ACCESS
  • Locked
    9.  Testing ML Pipelines
    5m 16s
    After completing this video, you will be able to outline the purpose and need for ML pipeline testing. FREE ACCESS
  • Locked
    10.  ML Pipeline Testing Tools and Frameworks
    4m 13s
    Upon completion of this video, you will be able to list ML pipeline testing tools and frameworks. FREE ACCESS
  • Locked
    11.  Course Summary
    33s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course CCSP 2022: Cloud Computing Concepts & Reference Architectures
Rating 4.6 of 164 users Rating 4.6 of 164 users (164)
Journey MLOps
Rating 4.7 of 6 users Rating 4.7 of 6 users (6)
Course AWS DevOps Engineer Professional 2021: CloudFormation
Rating 4.6 of 28 users Rating 4.6 of 28 users (28)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Visualizing Data for Impact: Introduction to Data Visualization
Rating 4.5 of 78 users Rating 4.5 of 78 users (78)
Course Visualizing Data for Impact: Visual Design Theory
Rating 4.4 of 68 users Rating 4.4 of 68 users (68)
Course Developing an AI/ML Data Strategy: The Data Analytics Maturity Model
Rating 4.6 of 260 users Rating 4.6 of 260 users (260)