Dr. Nick Ball is a generalist data scientist who joined Paperspace in November 2020. He has been using machine learning since 2000, first in academia (astrophysics), then in Silicon Valley since 2013.
In the fifth part of this six-part series, we will show how to deploy the model using Gradient Workflows and its integration with TensorFlow Serving.
In the fourth part of this six-part series, we will improve the result from the model in Part 3 by tuning some of its hyperparameters and demonstrate how the training process can be done in Gradient Workflows.
In the third part of this six-part series, we will use the TensorFlow Recommenders library to build a basic recommender model and train it on the data we prepared in Part 2.
For many organizations, a major question faced by data scientists and engineers is how best to go from the experimentation stage to production. In the second part of a six-part series, we'll show how to go from a raw dataset to a suitable model training set using TensorFlow 2 and Gradient notebooks.
For many organizations, a major question faced by data scientists and engineers is how best to go from the experimentation stage to production. In this first part of six, we outline the most important part of any enterprise data science analysis – posing a business problem.
Deep learning techniques receive much of the attention lately but classical ML techniques like GBTs are sometimes faster and easier to implement. Here we'll take a look at training a model with GBTs and then we'll deploy that model to an endpoint!
