Model Sync Across Clouds - Learn Data Science with Travis

Model-Sync-Across-Clouds

Building Machine Learning models in the cloud: A paradigm shift

Towards Data Science

Building machine learning models in the cloud: A paradigm shift Distinguishing between persistent and ephemeral compute for machine learning development Photo by Pero Kalimero on Unsplash In 2017, I ...

Demystifying training and serving models on cloud

Analytics Vidhya

Set up, deploy and serve a machine learning model on the cloud(Azure Kubernetes Service)

Simple way to deploy machine learning models to cloud

Towards Data Science

A simple workflow for machine learners looking to deploy their models as web-service

Training Keras models with TensorFlow Cloud

Keras Developer guides

Introduction TensorFlow Cloud is a library that makes it easier to do training and hyperparameter tuning of Keras models on Google Cloud. Using TensorFlow Cloud's run API, you can send your model code...

Machine Learning Projects on the Cloud — Key Steps in the Process

Towards Data Science

Deploying an ML model on the cloud is definitely different from working with Jupyter notebooks on your system. But it is more a matter of understanding the cloud system and how ML solutions get…

Google Cloud Platform Custom Model Upload , REST API Inference and Model Version Monitoring

Analytics Vidhya

Let’s first create a sample model using python. We will be starting up a jupyter notebook instance on google cloud platform and develop the custom model.

Parallelizing Models

Codecademy

Model parallelization in PyTorch allows the training of deep learning models that require more memory than a single GPU can provide. The model is divided into different parts (e.g., layers or modules)...

Training Keras models with TensorFlow Cloud

TensorFlow Guide

TensorFlow Cloud is a Python package that provides APIs for a seamless transition from local debugging to distributed training in Google Cloud. It simplifies the process of training TensorFlow models ...

Deployment Models in Cloud Computing

Towards Data Science

Introducing the three deployment models in cloud computing and understanding the difference between public cloud, private cloud and hybrid cloud

Big data for training models in the cloud

Towards Data Science

What happens when our training data is too big to fit on our machine, or training the model starts to take hours? We go to the cloud, of course! When you have a lot of data, such that you can’t…

Keras Hyperparameter Optimization in the Cloud

Towards Data Science

When training machine learning models it is often more convenient (and necessary) to offload the computation to a remote server. While free services such as Google Colab and Azure Notebooks are great…...

What is Cloud Computing? The Key to Putting Models into Production

Towards Data Science

A key skill for any Data Scientist is the ability to write production-quality code to create models and deploy them into cloud environments. Typically, working with cloud computing and data…