Beginning MLOps with MLFlow
Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure
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Beschreibung
Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training.
The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks.
You will:
- Perform basic data analysis and construct models in scikit-learn and PySpark
- Train, test, and validate your models (hyperparameter tuning)
- Know what MLOps is and what an ideal MLOps setup looks like
- Easily integrate MLFlow into your existing or future projects
- Deploy your models and perform predictions with them on the cloud
Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training.
The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks.
What You Will Learn
- Perform basic data analysis and construct models in scikit-learn and PySpark
- Train, test, and validate your models (hyperparameter tuning)
- Know what MLOps is and what an ideal MLOps setup looks like
- Easily integrate MLFlow into your existing or future projects
- Deploy your models and perform predictions with them on the cloud
Data scientists and machine learning engineers who want to learn MLOps and know how to operationalize their models
Covers the concepts behind MLOps that you need to know to operationalize your machine learning solutions for practical use Shows you how to deploy models with AWS SageMaker, Google Cloud, and Microsoft Azure Explains MLFlow with PyTorch, Keras, and TensorFlow
Autor*in
Sridhar Alla
Themen in »Beginning MLOps with MLFlow«
Python Production Machine Learning Operations ML Ops Microsoft Azure Google Cloud AWS SageMaker Deep Learning Data Science a2ML