Mastering MLOps: Your journey begins - Databricks教程

内容

• Introduction Introduction

  • Reduce time to production with MLOps

    55 秒
• 1. MLOps Components and Principles 1. MLOps Components and Principles

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    MLOps components

    3 分钟 14 秒
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    MLOps principles

    2 分钟 49 秒
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    MLOps components on Databricks

    47 秒
• 2. MLflow 2. MLflow

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    Introduction to MLflow

    2 分钟
  • Set up the environment and prepare the dataset

    3 分钟 28 秒
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    MLflow experiment tracking

    2 分钟 27 秒
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    Model logging in MLflow

    2 分钟 28 秒
  • Custom models in MLflow

    3 分钟
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    Register models in Unity Catalog

    4 分钟 9 秒
• 3. Feature Engineering on Databricks 3. Feature Engineering on Databricks

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    Introduction to feature engineering

    1 分钟 20 秒
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    Feature table and feature function

    2 分钟 50 秒
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    Train, log, and register models that use on-demand features

    2 分钟 45 秒
• 4. Feature and Model Serving 4. Feature and Model Serving

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    When to use Feature Serving vs. Model Serving

    2 分钟 35 秒
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    Create a FeatureSpec for Feature Serving

    2 分钟 57 秒
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    Deploy serving endpoints

    4 分钟 20 秒
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    Send requests to the serving endpoints

    3 分钟 8 秒
• 5. Deploy Application Using Databricks Asset Bundles 5. Deploy Application Using Databricks Asset Bundles

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    Getting started with DABs

    3 分钟 54 秒
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    Define Databricks workflow components

    3 分钟 53 秒
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    Define and deploy the Databricks Asset Bundle

    3 分钟 56 秒
• 6. Monitor Your Endpoints Using Inference Tables and Lakehouse Monitoring 6. Monitor Your Endpoints Using Inference Tables and Lakehouse Monitoring

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    Introduction to Lakehouse Monitoring

    4 分钟 47 秒
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    Set up, analyze, and process the inference table

    3 分钟 29 秒
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    Set up Databricks Lakehouse Monitoring

    3 分钟 33 秒
• Conclusion Conclusion

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    Mastering MLOps: Your journey begins

    34 秒