Tag

learning

Training and Deployment Pipeline, Part 2

From Deep Learning Patterns and Practices by Andrew Ferlitsch

This article covers:

Feeding models training data in a production environment.
Scheduling for continuous retraining.
Using version control and evaluating models before and after deployment.
Deploying models for large scale on-demand and batch requests, in both monolithic and distributed deployments.

Training and Deployment Pipeline

From Deep Learning Patterns and Practices by Andrew Ferlitsch

This article covers:

●      Feeding models training data in a production environment.

●      Scheduling for continuous retraining.

●      Using version control and evaluating models before and after deployment.

●      Deploying models for large scale on-demand and batch requests, in both monolithic and distributed deployments.

Persisting Application Data over Time

From The Well-Grounded Python Developer by Doug Farrell

This article, excerpted from chapter 10, covers

§ Persisting Data

§ Database Systems

§ Database Structures

§ Modeling Data with SQLAlchemy

Machine Learning as a Pipeline

From Deep Learning Patterns and Practices by Andrew Ferlitsch

Like the best software engineering, modern deep learning uses a pipeline architecture based on reusable patterns.

Deploying Machine Learning Models, Part 5: deployment

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Deploying Machine Learning Models, Part 4: creating a Docker image

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Deploying Machine Learning Models, Part 3: managing dependencies

From Machine Learning Bookcamp by Alexey Grigorev In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it…. Continue Reading →

Deploying Machine Learning Models, Part 2: model serving

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Deploying Machine Learning Models, Part 1: saving models

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Sentiment Classification Using a Large Movie Review Dataset, Part 2

From Machine Learning with TensorFlow, Second Edition by Chris Mattmann This article covers: Building sentiment classifier using logistic regression and with softmax Measuring classification accuracy Computing ROC curve and measure classifier effectiveness Submitting your results to the Kaggle challenge for… Continue Reading →

© 2022 Manning — Design Credits