Deep Learning with Docker - Affine Layer
Docker is a way to statically link everything short of the Linux kernel into your application. Because you can access GPUs while using a Docker container, it's also a great way to link Tensorflow or any dependencies your machine learning code has so anyone can use your work. You can distribute a reproducible machine learning project that requires little to no setup on the part of the user, so inst
How to deploy Machine Learning models with TensorFlow. Part 1 — make your model ready for serving.
After finishing the Deep Learning Foundation course at Udacity I had a big question — how did I deploy the trained model and make predictions for new data samples? Fortunately, TensorFlow was developed for production and it provides a solution for model deployment — TensorFlow Serving. Basically, there are three steps — export your model for serving, create a Docker container with your model and d
Deployment of Pre-Trained Models on Azure Container Services
This post is authored by Mathew Salvaris, Ilia Karmanov and Jaya Mathew. Data scientists and engineers routinely encounter issues when moving their final functional software and code from their development environment (laptop, desktop) to a test environment, or from a staging environment to production. These difficulties primarily stem from differences between the underlying software environments
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