Learn MLOps for Machine Learning

سرفصل های دوره

Introduction

001. Learn MLOps for Machine Learning Introduction

Lesson 1 Learning the MLOps Pipeline

001. Learning objectives

002. 1.1 Gather the data

003. 1.2 Analyze the data

004. 1.3 Prepare the data

005. 1.4 Train a model

006. 1.5 Evaluate the model

007. 1.6 Validate the model

008. 1.7 Deploy the model

009. 1.8 Monitor the model

Lesson 2 Handling the Data

001. Learning objectives

002. 2.1 Determine what the data sources are

003. 2.2 Create ETL pipelines to compile the data

004. 2.3 Understand the data schema with respect to the model

005. 2.4 Identify data that can be used for the model

006. 2.5 Perform feature engineering

007. 2.6 Version the data with DVC

008. 2.7 Make multiple data sets

009. 2.8 MLOps best practices for data

Lesson 3 Creating a Model

001. Learning objectives

002. 3.1 Use common Python libraries

003. 3.2 Code versioning with Git

004. 3.3 Perform hyperparameter tuning

005. 3.4 Track experiments with MLFlow

006. 3.5 Track experiments with DVC

007. 3.6 Evaluate the models

Lesson 4 Working with Production Models

001. Learning objectives

002. 4.1 Decide the best deployment method

003. 4.2 Test on pre-production environments

004. 4.3 Deploy to production

005. 4.4 Monitor the model for drift

006. 4.5 Validate the pipeline flow

007. 4.6 Automation points in MLOps

008. 4.7 Set up redeploy pipeline

Summary

001. Learn MLOps for Machine Learning Summary

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تولید کننده: OReilly