What is Inferrd?
It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.
Inferrd is a tool in the Machine Learning as a Service category of a tech stack.
Who uses Inferrd?
Pros of Inferrd
Easy to use
Very quick response time
- Machine Learning practioners don't need to wait for engineering to deploy their models anymore
- No need to invest in expensive infrastructure and tooling. Inferrd starts at $14, batteries included
- Built with security in mind. Your models are protected using state-of-the-art encryption at rest.
Inferrd Alternatives & Comparisons
What are some alternatives to Inferrd?
See all alternatives
A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
Azure Machine Learning
Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
Amazon Machine Learning
This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
Amazon Elastic Inference
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.