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.
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.
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
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.
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.
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.
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 →
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.
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.
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 →