Getting Started with MLOps

MLOps    |    Beginner
  • 11 videos | 1h 27m 21s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
MLOps is the integration of machine learning (ML) with DevOps, focusing on streamlining the end-to-end machine learning life cycle. It emphasizes collaboration, automation, and reproducibility to deliver reliable and scalable machine learning solutions. By implementing MLOps practices, organizations can efficiently manage and govern their machine learning workflows, leading to faster development cycles, better model performance, and enhanced collaboration among data scientists and engineers. In this course, you will delve into the theoretical aspects of MLOps and understand what sets it apart from traditional software development. You will explore the factors that affect ML models in production and gain insights into the challenges and considerations of deploying machine learning solutions. Next, you will see how the Machine Learning Canvas can help you understand the components of ML development. You will then explore the end-to-end machine learning workflow, covering stages from data preparation to model deployment. Finally, you will look at the different stages in MLOps maturity in your organization, levels 0, 1, and 2. You will learn how organizations evolve in their MLOps journey and the key characteristics of each maturity level.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Provide an overview of devops and mlops
    Outline how mlops works
    Identify the features of mlops
    Recognize the challenges of mlops
    Outline the use of the machine learning canvas
  • Outline the ml workflow
    Recognize ml architectural patterns
    Outline level 0 of ml pipelines
    Outline level 1 and 2 of ml pipelines
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 59s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 12m 3s
    After completing this video, you will be able to provide an overview of DevOps and MLOps. FREE ACCESS
  • Locked
    3.  What's Different About MLOps?
    10m 24s
    Upon completion of this video, you will be able to outline how MLOps works. FREE ACCESS
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    4.  Factors Affecting Machine Learning (ML) Models in Production
    8m 22s
    After completing this video, you will be able to identify the features of MLOps. FREE ACCESS
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    5.  Solving Machine Learning Problems
    4m 19s
    Upon completion of this video, you will be able to recognize the challenges of MLOps. FREE ACCESS
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    6.  The Machine Learning Canvas
    12m 53s
    After completing this video, you will be able to outline the use of the machine learning canvas. FREE ACCESS
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    7.  End-to-end Machine Learning Workflow
    6m 59s
    Upon completion of this video, you will be able to outline the ML workflow. FREE ACCESS
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    8.  ML Workflow Architectural Patterns
    11m 20s
    After completing this video, you will be able to recognize ML architectural patterns. FREE ACCESS
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    9.  Stages in MLOps Maturity Level 0
    5m 58s
    Upon completion of this video, you will be able to outline level 0 of ML pipelines. FREE ACCESS
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    10.  Stages in MLOps Maturity Level 1 and 2
    9m 38s
    After completing this video, you will be able to outline level 1 and 2 of ML pipelines. FREE ACCESS
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    11.  Course Summary
    3m 27s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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