Training Tensorflow Models on Jupyter Notebooks

Jupyter Notebooks

Jupyter notebooks are a popular tool for data scientists and researchers to create and share documents that contain live code, equations, visualizations, and narrative text. They are an incredibly powerful tool for interactively developing and presenting data science projects. Jupyter notebooks can be used for various use cases such as data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and more. They allow you to easily share your work with others by exporting your notebook as a PDF or HTML file. Jupyter notebooks also have a large community of users who have contributed many libraries and extensions that can be used to enhance workflows.

Tensorflow Models

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. TensorFlow helped launch the deep learning revolution: with pre-trained models, data, and high-level APIs, it has become easy for everyone to build ML and DL models. One of the important Tensorflow features is that it is flexible in its operating model, meaning it has modularity and the parts of it that you want to make standalone it offers you that option. TensorFlow.js is a JavaScript-based framework to run machine learning models within the browser. Any modern browser can run the TensorFlow model with no changes to the code.

Jupyter notebooks are an extremely popular tool for data scientists, analysts, and engineers alike to experiment with Tensorflow models before productionizing them. Kaspian securely hosts a performant and configurable JupyterHub instance, perfect for data teams who want to work with these models without wasting time setting up or managing the associated notebooking or compute infrastructure.

Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.